1. science
2. mathematics
3. statistics

Specialization Patterns in International Trade⇤
Walter Steingress
February 11, 2015
Abstract
I document new facts on the pattern of international specialization by looking at export and
import concentration. As a result of international trade, countries normally specialize in a few sectors, which tend to get exported, and diversify the importing sectors. To measure specialization,
I compute concentration indexes for the value of exports and imports and decompose the overall
concentration into the extensive product margin (number of products traded) and intensive product margin (volume of products traded). Using detailed product-level trade data for 160 countries,
I find that exports are more concentrated than imports, specialization occurs mainly on the intensive product margin, and larger economies have more diversified exports and imports because
they trade more products. Based on these novel facts, I assess the ability of the Eaton-Kortum
model, the workhorse model of modern Ricardian trade theory, to account for the observed patterns. The results show that specialization through comparative advantage induced by technology
differences can explain the qualitative and quantitative facts. Also, I evaluate the role of the key
determinants of specialization: the degree of comparative advantage, the elasticity of substitution
and geography.
Keywords: Ricardian Trade Theory, Comparative Advantage, Specialization, Import Concentration, Export Concentration
⇤I
thank Kristian Behrens, Andriana Bellou, Rui Castro, Jonathan Eaton, Stefania Garetto, Ulrich Hounyo, Joseph
Kaboski, Raja Kali, Baris Kaymak, Michael Siemer, Ari Van Assche, Silvana Tenreyro and Michael Waugh for their useful comments and suggestions. This paper also benefited greatly from comments by seminar participants at Boston
University, Carleton University, Georgetown, the University of Laval, HEC Montreal and the University of Montreal.
All errors are my own. Contact: Banque de France, 31 Rue Croix des Petites Champs, Paris 75001, France (e-mail:
walter.steingress@banque-france.fr).
1
1
Introduction
The pattern of specialization is at the core of international trade theory. A consequence of international trade is that countries do not need to produce all their goods, instead they can specialize in
the production of certain goods in exchange for others. Trade theory offers different explanations
of how countries specialize in the number and sales volume of goods. Assessing the empirical
relevance of the underlying theory is of vital interest since it not only allows evaluating the gains
from trade through specialization but also informs how the trade pattern affects the structure of
an economy. For example, a high degree of specialization increases the likelihood that product
specific shocks have aggregate effects in terms of output volatility and/or an impact on the terms
of trade.
The contribution of the paper is twofold. Firstly, it uncovers new facts on the pattern of specialization by looking at export and import concentration. It decomposes the overall level of concentration into a measure for the extensive and intensive product margin and documents concentration levels for exports and imports on all margins. The extensive product margin indicates
the degree of specialization in the number of goods traded. The concentration index on the intensive margin measures specialization in the volume of goods traded. Secondly, the paper evaluates
the Eaton and Kortum (2002) model’s ability to account for the observed specialization patterns.
Specifically, it assesses the model based on three basic questions about specialization: What explains the level of specialization in exports and imports? What determines the gap between specialization in exports and imports? Does specialization occur on the intensive or extensive product
margin?
Based on detailed product-level trade data for 160 countries, the results show that, on average,
countries specialize more in exports relative to imports, with Gini coefficients of 0.98 and 0.91 respectively. The decomposition reveals that specialization of exports occurs predominately on the
extensive margin. Countries receive their export revenues from few products. At the same time,
countries import a wide range of products but concentrate their expenditure towards a small number of products. Hence specialization of imports is driven by the intensive margin. The difference
between the concentration levels of exports and imports is due to the extensive margin. Countries
specialize in exporting few goods and diversify on imports by acquiring various products from
abroad. Focusing on cross-country differences, I find that larger economies have more diversified
imports and exports. This is mostly along the extensive margin, i.e. large economies export and
import a wider product range.
Having documented the observed specialization pattern, I employ a standard Ricardian trade
model developed by Eaton and Kortum (2002) to evaluate its ability to reproduce the stylized facts.
A key implication of this model is that it uncovers how comparative advantage due to technology
differences determines specialization endogenously on both the extensive and the intensive prod-
2
uct margins. Furthermore, it identifies geography together with the elasticity of substitution and
the degree of absolute and comparative advantage as the main determinants of specialization. A
higher level of technology increases a country’s absolute advantage and diversifies the extensive
margin of exports by broadening the product range it exports. The degree of comparative advantage heightens the sensitivity of concentration to changes in unit costs, thereby dictating specialization on both margins. Trade costs decrease comparative advantage and increase specialization
on the extensive and intensive margin. A higher elasticity of substitution provides for better substitution between intermediate goods and allows countries to concentrate their expenditure in low
price sectors. As a consequence, concentration on the intensive margin increases.
To calibrate the model, I follow Waugh (2010) and use data and the structure of the model to infer trade costs, technology, the elasticity of substitution and the degree of comparative advantage.
Not surprisingly, the simulated results show that the model produces the observed specialization
pattern qualitatively with countries being specialized in exports and diversified in imports on all
margins. More importantly, the simulated model also reproduces the degree of concentration on
the extensive versus the intensive margin for both, exports and imports. However, the obtained
levels for exports are too high in comparison to the data. Focusing on the variation across countries, the simulated model replicates the fact that larger economies are more diversified in exports
but fails to account for the observed cross-country pattern of imports.
At this point, it is important to note that the Ricardian model shares with other models of international trade, most notably monopolistic competition models based on Krugman (1980) and
Armington models like Anderson and Van Wincoop (2003), the ability to develop quantitative predictions about specialization patterns on the intensive and extensive product margin. However, in
these models tradable goods are differentiated by location of production since each country is the
sole producer of a good. Countries specialize completely and demand all product country combinations. When applying this definition of the product space to the data, the analysis shows that
countries are more concentrated in imports than in exports because they import only a small subset of available goods. This result implies that the empirical implications depend on the definition
of the product space, i.e. differentiated versus homogenous goods. Consistent with the Ricardian
model, the main empirical analysis is based on assumption that foreign varieties are perfect substitutes to domestic ones and local producers compete directly with imports for the lowest price.
The robustness section discusses the alternative results based on the Armington assumption.
This paper contributes to the international trade literature that analyses the relationship between the pattern of trade and specialization in commodities. Starting with MacDougall (1951),
Balassa (1963), Golub and Hsieh (2000) and Costinot et al. (2012) test the Ricardian prediction
that countries export relatively more of the commodities they are relatively more productive in.
Unlike these papers, my analysis does not intend to explain why countries specialize in a certain
commodity or group of commodities. Instead, it uses the level of concentration in trade data to
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shed light on the factors that drive specialization in the number and the volume of goods traded.
The levels of concentration in each trade direction contain information on the pattern of trade and
as such they provide a new quantitative test of the extent of specialization observed in the data.
The analysis presented in this paper also relates to a growing literature in quantifying the
importance of Ricardian comparative advantage in explaining trade patterns using the EatonKortum framework, see, for example, Chor (2010), Shikher (2011), Levchenko and Zhang (2011)
and Costinot et al. (2012). These papers specify a multi-sector Ricardian model with both interand intra-industry trade in order to derive implications on the sectorial level. In contrast, I abstract
from intra-industry trade and attach a sectoral interpretation to the continuum of traded goods
within the standard Eaton-Kortum framework. Given this notion, the number of traded sectors
arises endogenously and is not fix as in the previous papers. While the standard model has been
primarily used to explain bilateral trade flows and trade volume, (see, for example, Eaton and
Kortum (2002), Alvarez and Lucas (2007) and Waugh (2010)), I focus on the implications on the
pattern of trade and analyze how geography, tastes and absolute and comparative advantage
induce countries to specialize in narrow sectors.
Finally, my investigation adds to the empirical growth literature that analyzes the relationship
between income and trade patterns on the intensive and extensive product margins, see Hummels and Klenow (2005) and Cadot et al. (2011). Contrary to the previous papers, I apply the
decomposition also to imports and use the resulting empirical evidence to assess the ability of the
Eaton-Kortum model to explain the relationship of income differences and the concentration of
exports and imports along both margins. While Hummels and Klenow (2005) stress that models
with Krugman firm-level product differentiation can explain why larger economies export more
goods, my analysis shows that the Ricardian model of Eaton-Kortum offers an alternative framework to describe the observed patterns. The novel approach of linking cross-country variation of
export and import concentration to test the Eaton-Kortum model sheds light on how the interaction between preferences, technology and geography establishes trade patterns on the intensive
and extensive product margin. As such, the Eaton-Kortum framework can provide theoretical
guidance for future work.
The rest of the paper is organized as follows. Section 2 describes the data and presents the
empirical evidence of import and export concentration. Section 3 lays out the theoretical framework. Section 4 describes the calibration that allows the model to replicate the empirical facts.
Section 5 estimates trade costs and presents the simulation results based on the estimated trade
costs. Section 6 discusses the robustness of the results while section 7 concludes.
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2
Empirical evidence and data
The starting point of my analysis is an empirical assessment of the observed specialization patterns in world trade using detailed product level trade data. Before describing the data and the
empirical evidence, we examine the properties of the concentration measurements used, which
form the basis of the qualitative and quantitative tests of the model.
2.1
Concentration measurements
I compute two measures of specialization for product level sales, the Gini coefficient and the Theil
index. The Theil index has the advantage of being decomposable into an extensive and intensive
product margin measure. For concreteness, I focus on exports - concentration measures for imports are entirely analogous. The two measurements are defined as follows. Let k index a product
among the N products in operation in the world economy, let Rk be the corresponding export sales
revenue, say, in a given country. The export Gini in this country is defined as :
G=
2 (ÂkN=1 kRk )
N ÂkN=1 Rk
N+1
N
(1)
where export revenues of product k, Rk , are indexed in increasing order, i.e. Rk < Rk+1 , and N
denotes the total number of products in the world. A Gini coefficient of zero expresses complete
diversification across trade revenues, i.e. (1) a country exports all products and (2) the revenues
are the same across them. An index of one expresses complete specialization in which case export
revenues stem from one product only. Alternatively, the Theil index is a weighted average of the
log difference from the mean export revenue ( R̄) and defined by the following formula
1
T=
N
Â
k2 N
Rk
ln
R̄
✓
Rk
R̄
◆
(2)
The index takes the value of zero in the case of complete diversification and ln( N ) in the case
of complete specialization. Cadot et al. (2011) decompose the Theil index into a measure for the intensive and extensive product margin, T = T ext + T int . The extensive Theil index ( T ext ) captures
the concentration in the number of products (extensive product margin) whereas the intensive
Theil ( T int ) measures the concentration in the sales volume of products (intensive product margin). The intensive Theil index is given by:
T int =
1
Nx
Â
k2 Nx
Rk
ln
R̄ x
✓
Rk
R̄ x
◆
(3)
and the extensive Theil index is
T
ext
= ln
✓
N
Nx
◆
(4)
Nx denotes the number of exported products and R̄ x represents the mean value of exported products.
5
2.2
Data
To build my empirical evidence, I use the BACI data set provided by CEPII (Gaulier and Zignago
(2009)) and choose the 6 digit HS 1992 product classification scheme is the preferred level of disaggregation. I follow Hummels and Klenow (2005) and refer to import flows of the same 6-digit
product from different trading partners to different varieties of the same product. I assume that
the tradable goods sector corresponds to manufactures defined to be the aggregate across all 34
BEA manufacturing industries, see Feenstra et al. (1997).1 Using a correspondence table provided
by Feenstra et al. (1997), I identify 4529 tradable manufacturing products. The baseline sample
covers 160 countries representing all regions and all levels of development between 1992 and 2009
(18 years). In total, the sample consists of 2880 observations (country-years).
Note the data contains import and export flows within 6 digit product categories. The model
I am assessing is Ricardian and does not feature trade between varieties of the same product. To
establish a mapping between the model and the data, I net out the within product component
by considering net trade flows instead of gross trade flows.2 To measure the importance of trade
between products and trade between varieties, I follow Grubel and Lloyd (1975) and calculate the
percentage share of trade between products with respect to total trade. I find an average value of
81 percent across countries. The overall share of total net trades flows with respect to total gross
trade flows is 65 percent. Both findings suggest that the majority of trade flows between countries
in this sample is across products.3
Based on net trade flows at the product level, I calculate concentration indexes for each country
on all margins for each year and then take the average over the whole sample period. Because the
concentration indices employed are independent of scale, the calculation on a year-to-year basis
avoids the need to deflate the data. Figure 1 plots the mean export against the mean import
concentration for each country together with the 45 degree line. In terms of overall concentration,
Figures 1(a) and 1(b), the vast majority of observed levels lie above the 45 degree line highlighting
the fact that exports are more concentrated than imports for almost all countries. On the intensive
product margin, Figure 1(c), the specialization level of exports is similar to imports with slightly
higher levels of concentration for exports. Figure 1(d) plots the results for the extensive product
margin with countries exporting fewer products than they import.
Table 1 summarizes the sample statistics with the average year-by-year indices over the 2880
1 This
is a simplification, but it is reasonable as a first-order approximation because, for all countries in the sample,
this represents on average 76 percent of all merchandise imports; the median is 91 percent.
2 I compute total net exports at the 6 digit product level and consider a country as an exporter of that product if net
exports are positive and an importer otherwise.
3 In the appendix I present an alternative approach to account for observed intra-industry trade in the data. The
basic idea is to develop a measurement device that enables the model to characterize trade within and across products.
The suggested procedure converts the product units in the model to product units in the data and allows examining
specialization patterns based on gross trade flows. In the rest of the paper, I follow the net trade flow approach. I present
the estimation and results of the alternative procedure in the appendix.
6
Table 1: Mean concentration indexes over 2880 country-year pairs
Gini
Level of
concentration
% share of overall
concentration
Theil Exports (X)
Theil Imports (M)
Exports
Imports
Extensive
Margin
Intensive
Margin
Total
Extensive
Margin
Intensive
Margin
Total
0.98
0.91
2.60
2.13
4.73
1.10
1.61
2.71
55%
45%
40%
60%
country-year pairs. As implied by Figure 1, exports are more concentrated than imports on all
margins. With respect to overall concentration, the summary statistics reveal high levels of export
and import concentration with a Gini coefficient of 0.98 for exports and 0.91 for imports. In the case
of exports, the high level of concentration is due to the fact that countries export few products and
hence specialization is primarily driven by the extensive margin. For imports, the decomposition
favors an alternative explanation. Countries import a fairly wide range of products but concentrate their trade in the value of few products. Focusing on the gap between export and import
concentration, Table 1 shows that differences between exports and imports are mainly explained
by the extensive margin. The Theil of 1.10 on the extensive margin of imports implies that, on average, a country net imports a 33.3 percent of all products. On the other hand, the extensive Theil
of exports indicates that a country net exports 7.4 percent of the product space. In terms of the
intensive margin, a country receives roughly 50% of its export revenues from 1% of the products
it exports and spends 50% of its import expenditure on 2% of the products it imports. Overall,
these results are consistent with the idea that openness to trade spurs countries to specialize in
few exporting sectors and diversify the importing sectors.
Turning to cross country differences, the empirical evidence shows that larger economies diversify more than smaller economies. Figure 2 plots the log of the mean levels of concentration as
a function of market size including the best linear fit for all margins. Market size is measured by
the log of the average GDP relative to the United States (USA = 0). As Figures 2(a) and 2(b) show,
the overall Theil index decreases with respect to relative GDP, i.e. smaller economies specialize
more. This relationship is more pronounced for exports than for imports with an R square of 0.58
compared to 0.41. The decomposition reveals that specialization on the intensive margin does
not vary with market size for both, exports (Figures 2(e)) and imports (2(f)). The main driver of
specialization differences across countries is the extensive margin. Particularly robust is the linear
relationship on the extensive margin for exports with an R square of 0.75. Bigger economies are
more diversified because they export more products, which is consistent with Koren and Tenreyro
(2007)’s observation that larger economies are more diversified because they produce and export
more products. The relationship between market size and specialization on the extensive margin
of imports follows a L shape pattern. As the size of an economy increases, countries diversify
7
on imports until reaching a certain market size after which concentration is roughly equal across
countries.
At this point, the key qualitative and quantitative facts have been established. First, exports
are more specialized than imports. Second, the extensive margin drives concentration of exports
and the intensive margin for imports. Third, the target levels of concentration are displayed in
Table 1. Fourth, the cross-country patterns imply a negative relationship between market size
and specialization caused by the extensive margin, i.e. larger economies export and import more
products. The rest of the paper evaluates the Ricardian model’s ability to account for these stylized
facts. Next, I present the relevant parts of the Alvarez and Lucas (2007) extension of the EatonKortum framework.
3
Model
The Eaton–Kortum model is Ricardian, with a continuum of goods produced under a constantreturns technology. In this paper, we focus on the Alvarez and Lucas (2007) model and include
capital as in Waugh (2010). Next, I derive the relevant theoretical predictions on the pattern of
trade and evaluate the importance of the key model parameters for specialization of imports and
exports.
Consider a world economy with I countries, where each country produces tradable intermediate goods as well as non-tradable composite and final goods. Following Alvarez and Lucas (2007),
define x = ( x1 , ..., x I ) as a vector of technology draws for any given tradable good and refer to it
I . The production of an intermediate good in country i is defined by:
as “good x” with x 2 R+
qi ( xi ) = xi q [kai s1i
] qmi 1
a b
b
.
Technology xi differs between goods and is drawn independently from a common exponential
distribution with density f and a country specific technology parameter li , i.e. xi ⇠ exp(1/li ).
Denote the interest rate by ri , the wage by wi and the price of the intermediate aggregate good by
pm,i . The intermediate good sector is perfectly competitive. Intermediate good producers minimize input costs and sell the tradable intermediate good at price
pi ( xi ) = Bxiq [ria wi1
where B = b
b (1
b)
(1 b ) .
a b 1 b
] pmi .
The continuum of intermediate input goods x enters the production
of the composite good qi symmetrically with a constant elasticity of substitution (h > 0)
qi =
ˆ
0
•
q ( x )1
1/h
8
h/(1 h )
f( x )dx
.
The produced aggregate intermediate good qi can then be allocated costless towards the production of final goods or being used as an input in the production of intermediate goods. Similarly,
capital and labor can be used either to produce intermediate or final goods. Finally, consumers
draw their utility linearly from the final good. All markets are perfectly competitive. Since these
features are not central to the implications I derived in this paper, I omit them. The interested
reader is refereed to Alvarez and Lucas (2007) for the full description of the model.
3.1
General equilibrium
Once a country opens to international goods markets, the intermediate goods are the only goods
traded. Final goods are not traded and capital and labor are immobile between countries. Trading
intermediate goods between countries is costly. We define “Iceberg” transportation costs for good
x from country i to country j by kij where kij < 1 8 i 6= j and kii = 1 8i. As in Alvarez and
Lucas (2007), we also consider tariffs. wij is the tariff charged by country i on goods imported
from country j. Tariffs distort international trade but do not entail a physical loss of resources.
Incorporating the trade costs, composite good producers in country i will buy the intermediate
good x from country j that offers the lowest price
2
pi ( x ) = B min 4
j
3
a b 1 b
] pmj q
xj 5 .
kij wij
[r aj w1j
(5)
Equation 5 shows that whether country i specializes in the production of good x depends on the
productivity realizations, factor prices and trade costs. If country i does not offer a good at lowest
costs in the local market, the good is imported. Following Alvarez and Lucas, the resulting price
index of tradable goods in country i is
0
where A = G(1 + q (h
I
0
B
pmi = ( AB) @ Â @
j =1
b 1 b
w j pmj
kij wij
1
A
1/q
1
C
lj A
q
1)) is the Gamma function evaluated at point (1 + q (h
(6)
1)). Next, we
calculate the expenditure shares for each country i. Let Dij be the fraction of country i’s per capita
spending pmi qi on tradables that is spent on goods from country j. Then, we can write total spending of i on goods from j as
pmi qi Dij =
ˆ
Bij
pi ( x )qi ( x )f( x )dx
where Bij defines the set of goods country j attains a minimum in equation 5. Note that Dij is
simply the probability that country j is selling good x in country i at the lowest price and calculated
to be
9
1/q
Dij = ( AB)
0
@
a b 1 b
] pmj
[r aj w1j
pmi kij wij
1
1/q
A
lj.
(7)
Equation 7 shows that in this model the sensitivity of trade between countries i and j depends on
the level of technology l, trade costs w, geographic barriers k and the technological parameter q
(reflecting the heterogeneity of goods in production) and is independent of the elasticity of substitution h. This result is due to the assumption that h is common across countries and does not
distort relative good prices across countries. Note also that by the law of large numbers, the probability that country i imports from country j is identical to the share of goods country i imports
from j. In this sense, trade shares respond to costs and geographic barriers at the extensive margin:
As a source becomes more expensive or remote it exports/imports a narrower range of goods. It is
important to keep in mind that the number of intermediate input industries that enter the production of the composite good is fixed. Each country uses the whole continuum of intermediate goods
to produce composite goods. There are no gains of trade due to an increased number of varieties.
Welfare gains are realized through incomplete specialization. Domestic production competes with
imports and countries specialize through the reallocation of resources made available by the exit
of inefficient domestic producers.
To close the model, we impose that total payments to foreigners (imports) are equal to total
receipts from foreigners (exports) for all countries i
Li pmi qi
I
 Dij wij =
j =1
I
 L j pmj q j Dji w ji
(8)
j =1
The previous equation implies an excess demand system which depends only on wages. Solving this system, describes the equilibrium wage for each country together with the corresponding
equilibrium prices and quantities. Next, I describe the predictions on export and import concentration on both margins.
3.2
Concentration of exports and imports
In the model, the pattern of trade is established by domestic producers competing with importers
for selling intermediate goods in the local market. Given the equilibrium price, p( x ), and quantity,
q( x ), the total expenditure that country i spends (c.i.f.) on imported good x, RiM ( x ), is:
RiM ( x ) = Li pi ( x )qi ( x )
x2
/ Bii
I is the set of goods where country i obtains the minimum price at home. Equivawhere Bii ⇢ R+
lently, domestic producers export their good to all foreign markets where they attain the minimum
price. The set of exporting goods is simply a collection of the set of goods country i exports to any
destination j, x 2 [ jI6=i B ji . As a result, (f.o.b.) export revenue sales of good x, Ri,X ( x ), are given by:
10
RiX ( x ) =
I
 Lk pk (x)qk (x)kki wki
x 2 [ jI6=i B ji
k 6 =i
Given the described pattern of trade, the concentration index for imports is identified. To show
this, I decompose the overall concentration into a concentration measure for the intensive and
extensive product margin. Using equation 3, the Theil index for the concentration of imports on
the intensive production margin can be written as:
int
TiM
=
ˆ
x2
/ Bii
RiM ( x )
ln
R̄iM
✓
RiM ( x )
R̄iM
◆
f( x )dx
In the appendix I show that the distribution of import expenditures follows a Fréchet distribution
with shape parameter 1/q (h
1) and scale parameter si . Solving the integral, the intensive Theil
index of imports for country i becomes:
int
TiM
= ln (G(1 + q (1
ˆ
h )))
0
1
⇣
ln u(
q (1 h ))
⌘
e
u
du
(9)
where G(.) stands for the Gamma function. Import specialization on the intensive margin is independent of equilibrium prices, trade costs, geography and the level of technology l. It is solely
determined by preferences (i.e the elasticities of substitution) and heterogeneity in production (i.e.
the degree of comparative advantage). A higher elasticity of substitution (h ) increases specialization by allowing producers in the composite intermediate good sector to better substitute cheap
for expensive products and concentrate expenditure towards these sectors. Similar, an increase in
the degree of comparative advantage (q ), which corresponds to a higher variance of productivity
realizations and therefore an increase in unit price differences across goods, heightens the degree
of concentration.
To compute the concentration of imports on the extensive margin, note that the set of goods
produced is disjoint form the set of goods imported. Consequently, we can express the share
of goods imported as 1 minus the share of goods produced, (1
Dii ). The Theil index for the
extensive margin of imports is equal to :
ext
TiM
= ln
✓
N
NiM
◆
=
ln(1
Dii )
(10)
where
Dii = ( AB)
1/q
[ria wi1 a ]
pmi
!
b/q
li
and depends on the level of technology and equilibrium prices. To assess the level of specialization
in exports, I simulate the model within a discrete product space in the following section. I then
11
calculate the export concentration index on the intensive margin according to equation 3.
Having outlined the pattern of trade and the corresponding implications on the specialization
pattern of exports and imports, the next section discusses the simulation of the model. It contains
special cases of equilibria designed to spell out step-by-step the main implications of the model
on export and import concentration and in further instance on specialization.
4
Calibration and simulation
To simulate the theoretical model, which assumes an infinite amount of goods, I "discretize" the
Fréchet distribution of total factor productivity and calculate the respective trade value for each
product x. Concerning the parameters of the model, we need values for a, b, g, h and q. For a, b
and g, I use the same values as Alvarez and Lucas (2007). We set the capital share to a = 0.3, the
efficient labor share in the tradable goods sector to b = 0.5 and the labor share in the production
of non-tradable final goods to a = 0.75.
To calibrate the elasticity of substitution (h) and the variance of the productivity draws (q), I use
the model’s implication on the revenue and the quantity distribution of imports. As shown in the
previous section, the distribution of import expenditure follows a Fréchet distribution with shape
parameter, 1/q (h
1). Similarly, it can be shown that the distribution of quantities imported also
follows a Fréchet distribution with shape parameter, 1/(qh ). Using the fact that the Theil index on
the intensive margin solely depends on the shape parameter, we first calculate the average Theil
int = 1.61, and for imported quantities, T int = 3.58.4 Then, using
index for import expenditure, TiM
iQ
equation 9, we get the corresponding shape parameters and obtain 2 equations with 2 unknowns.
The solution of the system consists of an elasticity of substitution (h = 8) and a degree of comparative advantage (q = 0.10). The elasticity of substitution is high but still in the parameter range
found in the literature, see Broda and Weinstein (2006). The degree of comparative advantage also
lies in the parameter range 0.08 to 0.15 estimated in the literature, see Eaton and Kortum (2002).
However, compared to more recent estimates by Simonovska and Waugh (2011), q = 0.10 is rather
low.
In the following subsections, I analyze import and export concentration in special cases of the
equilibrium by assuming different trading schemes. Doing so builds intuition of how taste, technology and geography determine specialization. To illustrate the impact of each factor separately,
it is instructive to start the analysis by assuming symmetric countries and introduce heterogeneity across countries later on. Finally, I show that for a particular configuration of trade costs the
4 In
addition to the dollar value of imports, BACI also reports the volume of goods imported, which are measured in
tons. To calculate the Theil index of the volume of imports, we follow the procedure applied to the import revenues. We
int = 3.58 represents the
first calculate the net volume of imports for each good and then apply equation 3. The value TiQ
cross-country average over the sample period.
12
Eaton-Kortum model is able to replicate the specialization patterns observed in the data.
4.1
Symmetric countries
All countries are identical. Trade costs are symmetric and set to kij = k 8 i 6= j with kii = 1 and
wij = 1 8i, j. Due to symmetry, factor prices equalize across countries. The corresponding trade
share matrix D is symmetric and the (i, j) element is given by:
Di,j =
(k )1/q
1
8i 6= j and Di,i =
1/q
1 + ( I 1)(k )
1 + ( I 1)(k )1/q
In free trade, k = 1, each country’s intermediate good producers specialize in a distinct set of
goods equal to the relative size of the economy and export all products produced, Dii = Dij = 1/I.
The corresponding share of imported products is 1
1)/I. In this case, Ricardian
Dii = ( I
specialization forces are strongest and the gap between export and import concentration reaches
a maximum.
Concentration on the Extensive Margin Including trade costs, the concentration index
of imports equals the share of goods country i imports from all countries in the world and is given
by:
ext
TiM
=
ln((1
Dii )) = ln(1 + ( I
1)(k )1/q )
ln(( I
1)(k )1/q )
Concentration at the extensive margin of imports increases with trade barriers k and decreases
with the number of trading partners I
1 and the degree of comparative advantage q. Regarding
exports, the extensive Theil index is given by the number of products exported to any destination
divided by the total number of products in the world. To count the number of products exported,
define the set of products exported as the union of the set of products exported to each destination,
Uex = [ jI6=i B ji . Because the set of products exported to destination j overlaps with the set of
products exported to destination k, B ji \ Bki 6= ∆, I apply the Inclusion Exclusion principle to avoid
double counting. As I show in the appendix, under the assumption of symmetry, the extensive
Theil index of exports is given by:
ext
TiX
=
ln
I 1
Â(
k =1
1) k
1
I
k
!
ak
!
(11)
where the share of products exported to k destinations, ak , is given by:
ak =
(k )1/q
k + ( I k )(k )1/q
The concentration of exports increases with geographical barriers, the degree of comparative
advantage and the number of trading partners. In general, a larger number of trading partners
increases competition between production and imports in the domestic market resulting in the
13
Table 2: Simulated export and import concentration indexes for benchmark parameters.
Gini
Parameters
Exports
Imports
(k = 1)
( k = 0.7)
( k = 0.7, NT=10)
Data
0.99
0.99
0.98
0.98
0.72
0.77
0.86
0.91
Theil Exports (X)
Extensive Intensive
Margin
Margin
5.01
1.91
5.04
1.18
2.47
2.45
2.60
2.13
Total
6.92
6.22
4.92
4.73
Theil Imports (M)
Extensive Intensive
Margin
Margin
0.01
1.61
0.10
1.61
1.09
1.61
1.10
1.61
Total
1.62
1.71
2.70
2.71
production of fewer goods at home and an increase in the number of goods imported. Also, more
trading partners increase competition among exporters in foreign markets forcing countries to
specialize more on the extensive margin of exports. Impediments to trade, i.e. a reduction in
k, and a higher degree of comparative advantage, q, reduce import competition and, as a result,
fewer goods are exported and imported. Notice that in the special case of free trade all goods
produced are exported and concentration of production equals concentration of exports. With
trade costs, countries export a subset of produced goods leading to more concentration of exports
relative to production.
Concentration on the Intensive Margin
As noted previously the distribution of import
expenditure follows a Fréchet distribution and is pined down by the elasticity of substitution (h)
and the degree of comparative advantage (q). Concerning the distribution of export revenues,
the simulation shows that it depends positively on the elasticity of substitution (h), the degree
of comparative advantage (q) and geographical barriers (k ). The number of trading partners has
non-monotone effects on the concentration of exports at the intensive margin. Few trading partners leads to high level of concentration because high and low productive goods enter export
markets. However, as the number of trading partners increases, the degree of competition in the
export markets also increases and only products with high productivity are exported. In the case
of free trade, countries export all their goods to all destinations and, given that preferences are
identical, export and import concentration on the intensive margin equalize.
The results presented in Table 2 show that the free trade calibration of Alvarez and Lucas (2007)
is able to replicate the qualitative fact that, overall, exports are more concentrated than imports.
While the simulated overall level of export concentration attains the degree of specialization observed in the data, in the benchmark free trade parametrization countries diversify excessively in
imports because they import too many goods.
Next, I introduce 42 percent symmetric trade costs to all trading partners, k = 0.7. Row 3 of
Table 2 shows the results. Impediments to trade reduce the number of products exported and
imported and concentration on the extensive margin increases for both. Note that higher trade
14
costs lower the level of concentration on the intensive margin of exports. Due to the increase
in trade costs, only very efficient producers export and their export revenues are more evenly
distributed across products and trade partners. Still, the gap between export and import concentration remains substantial. The reason is that the degree of competition countries face in export
and domestic markets is too high. In the symmetric setting the only way to reduce competition is
to limit the amount of trading partners. Using equation 11, the number of trading partners (NT)
corresponding to the empirical Theil index is 10, see fourth row of Table 2. Limiting the number of
trading partners (NT) by introducing infinite trade costs to countries outside of the block reduces
competition in all markets. Less competition in the domestic market increases the survival rate
of local producers and reduces the amount of goods imported. Note that revenues of exporting
industries are now geographically more concentrated and hence specialization on the intensive
margin of exports intensifies.
In sum, with the introduction of symmetric trade costs, the model can replicate the mean levels
of concentration observed in the data. The key parameters are trade costs. In particular, by creating
trade blocks, which amounts to introduce zeros in the bilateral trade matrix, we can calibrate the
model to explain the mean pattern of specialization.
4.2
Asymmetric countries
In this section I analyze the effects of cross-country heterogeneity on specialization. The empirical
facts imply a negative relationship between specialization and market size. For this reason, I
introduce heterogeneity in technology li to reflect the observed GDP differences in the data. To
start with, consider equation 8 in a free trade equilibrium:
( wi L i + r i Ki ) =
N
Â
j =1
w j L j + r j K j D ji
which can be simplified to
⇣
li = C (wi Li + ri Ki ) wia ri1
a
⌘ bq
(12)
where C is a constant. Using equation 12, I back out the level of technology, as a function of GDP
(wi Li + ri Ki ) and endowments Li and Ki assuming that they are chosen optimal. To calibrate l,
I use GDP, capital and population data from the Penn World table. I follow Waugh (2010) and
normalize the obtained parameters for li , Li and Ki relative to the United States.
Concentration on the Extensive Margin Plugging in the equilibrium wage into equation 7,
I get the corresponding trade share matrix D with the (i, j) element given by:
D ji =
( wi L i + r i Ki )
8j
( wk Lk + r k Kk )
ÂkI =1
15
(13)
Equation 13 shows that under the assumption of free trade country i’s share of the number of
products exported is equal to its relative level of GDP with respect to world GDP. Hence, larger
economies export more and import less products compared to smaller economies. This result is at
odds with the empirical evidence. In the data, larger economies export and import more products.
4.3
Asymmetric trade costs
To reconcile the cross-country concentration differences for imports, I consider trade costs as a
function of either a fixed export cost (ex j ) or a fixed import cost (imi ). While both types of costs
can reconcile the fact that larger countries import more goods, the exposition focuses on the fixed
export cost.5 In this case, each country pays a country specific cost to export, which is independent
of the importing country j, k ji = exi 8i 6= j and ki,i = 1 8 j = i. Due to asymmetric trade costs,
wages and composite good prices do not equalize. The trade share matrix is asymmetric and given
by:
D ji = ( AB)
1/q
1 b ! 1/q
[wia ri1 a ] b pm,i
pmj exi
and
li 8i 6 = j
Dii = ( AB)
1/q
wia ri1
pm,i
a
!
b/q
li
Focusing on the expression for the share of goods that country i exports to country j, D ji , shows
that a higher export cost reduces the fraction of the good that arrives in destination j (exi #) and
decreases the number of goods country i exports to any destination j. Solving for the equilibrium
and assuming that composite good prices across countries are approximately equal, one can show
that the share of goods imported is approximately:
(1
Dii ) t
✓
1
1
q
C1 exi (wi Li + ri Ki )
◆
(14)
where C1 is a constant. Equation 14 shows that the share of goods imported is decreasing in the
country specific exporting costs, (∂(1
Dii )/∂exi > 0). Lower exporting costs allow producers
to pay higher factor prices at home and still be competitive in export markets. At the same time,
higher unit costs of production reduce competitiveness at home and result in a larger share of
imported goods. Hence, an exporter fixed effect can reconcile the fact that larger economies import
and export more goods.
The main difference between the import cost and the export cost in terms of import concentration lies in the implication on the price level of tradable goods. One can show that the export
cost implies a nearly constant price level of tradable goods across countries, see Waugh (2010). As
a result, unit cost differences between countries are predominantly driven by factor price differences. On the contrary, the import cost leads to large cross-country price level differences with
smaller economies facing a higher tradable price level. In this case, unit cost differences are driven
by factor as well as tradable goods price level differences. Based on Waugh (2010)’s results that
5 One
can apply the same reasoning to the fixed import cost.
16
Table 3: Simulated export and import concentration indexes for asymmetric countries.
Gini
Parameters
Exports
Imports
( k = 1)
( k = ex, NT=10)
Data
0.99
0.98
0.98
0.73
0.85
0.91
Theil Exports (X)
Extensive Intensive
Total
Margin
Margin
5.75
2.59
2.60
1.91
2.67
2.13
7.66
5.26
4.73
Theil Imports (M)
Extensive Intensive
Total
Margin
Margin
0.007
1.10
1.10
1.61
1.61
1.61
1.62
2.71
2.71
countries have similar price levels of tradable goods, I focus only on the case of the exporter fixed
effect for the rest of my analysis.
In sum, the introduction of asymmetric trade costs in form of a country specific cost to export or
import allows the model to replicate the import specialization pattern across countries, in particular when larger economies face relative low costs to either export or import. Waugh (2010) argues
that trade costs have to be asymmetric, with poor countries facing higher costs to export relative
to rich countries, in order to reconcile bilateral trade volumes and price data. While both our
approaches highlight the importance of asymmetric trade costs in explaining trade data, our analysis differs. Waugh uses the Eaton Kortum model to explain bilateral trade volumes and price data
whereas I look on the models implications on the specialization pattern of exports and imports.
In this respect, the results presented in this paper provide further evidence on the importance of
asymmetry in trade costs when studying trade volumes and trade patterns across countries.
Row 1 of table 3 presents simulations results in the case of asymmetric countries and free trade.
Note that in relation to the symmetric country case introducing technology differences increases
the mean level of concentration for exports and decreases the level of concentration for imports.
The underlying reason is that the technology distribution is skewed towards less productive countries and these countries export fewer and import more goods. Beside these changes, the results
are similar to the symmetric case.
To reconcile the empirical evidence that larger countries import more goods, I introduce country specific costs to export with larger economies facing relatively lower export costs. In particular,
I calculate the implied export cost from equation 14 by replacing the share of goods produced at
home by the extensive Theil index of imports observed in the data , Dii = 1
Ext ). Row
exp( TM
2 of table 3 shows the results of the corresponding mean concentration levels. In terms of the
cross country pattern, Figure 3 plots the simulated (in red) and the empirical (in blue) concentration levels against GDP for both margins. The figures show that the country specific export
cost in combination with technology and endowment differences can replicate the across country
evidence on all margins.
17
In the previous section I analyzed special cases of the equilibrium to study the different factors
that determine specialization in the Eaton Kortum model. The key determinants are the degree
of comparative advantage, the elasticity of substitution and asymmetric trade costs. However,
I treated trade costs as free parameters and showed that for a particular configuration of trade
costs, the model is able to reproduce concentration levels at the mean as well as the cross-country
specialization pattern for both exports and imports. In the next section, I estimate trade costs
and technology parameters based on bilateral trade shares using the model’s structure and check
whether for given trade shares the model is able to generate the observed specialization pattern in
the data.
5
Estimating trade costs from bilateral trade shares
The starting point of the estimation of technology and trade costs is a structural log-linear “gravity” equation that relates bilateral trade shares with trade costs and structural parameters of the
model. To derive the relationship, simply divide each country i’s trade share from country j, see
equation 7, by country i’s home trade share. Taking logs yields I
log
✓
Dij
Dii
◆
= Sj
Si +
1 equations for each country i :
1
1
log(kij ) + log(wij )
q
q
in which Si presents the structural parameters and is defined as Si = log([ria wi1
(15)
(1 b)/q
] b/q pmi
l i ).
In order to estimate trade costs k and technology l implied by equation 15 I use data on bilateral
trade shares across 160 countries. I follow Bernard et al. (2003) and calculate the corresponding
bilateral trade share matrix by the ratio of total gross imports of country i form country j, Mij ,
divided by absorption Absi
Dij =
a
Mij
.
Absi
Absorption is defined as total gross manufacturing output plus total imports, Mi , minus total
exports, Xi . To compute absorption, we use gross manufacturing output data from UNIDO.6
Combined with trade data from BACI, we get the expenditure share, Dij , which equals the value
of inputs consumed by country i imported from country j divided through the total value of inputs
in country i. Note that instead of focusing on a particular year, I compute the expenditure share
for each year of the period 1992 - 2009 and take the average expenditure share over the sample
period.7
6 The
details are in the appendix.
resulting sample consists of 160 times 159 potential observations if each country trades with all other countries.
In our sample the total number of observations is 9649 implying a large number of zeros in the bilateral matrix. For
this reason, I conduct a robustness test where I estimate the model with the Poisson estimator proposed by Silva and
Tenreyro (2006). The appendix presents the results.
7 The
18
Table 4: Estimation Results
Summary Statistics
Observations
9649
Geographical barriers
Barrier
[0,375)
[375,740)
[750,1500)
[1500,3000)
[3000,6000)
[6000,max)
Tariff
Shared border
In total there are only I 2
TSS
2,60E+05
SSR
4,67E+04
R2
0.82
Paremeter estimate
-4,89
-5,76
-6,78
-7,98
-9,05
-9,81
-0,23
1,37
Standard error
0,10
0,06
0,04
0,03
0,02
0,03
0,10
0,09
% effect on cost
79,93%
99,60%
125,62%
160,66%
196,42%
224,64%
5,47%
-15,19%
I informative moments and I 2 parameters of interest. Thus, restric-
tions on the parameter space are necessary. To create them, I follow Eaton and Kortum (2002) and
assume the following functional form of trade costs.
log kij = bij + dk + wij + ex j + eij
Trade costs are a logarithmic function of distance (dk ) a shared border effect between country i
and j (bij ), a tariff charged by country i to country j and an exporter fixed effect (ex j ). Tariff (wij )
represents the weighted average ad valorem tariff rate applied by country i to country j. The
distance function is represented by a step function divided into 6 intervals. Intervals are in miles:
[0, 375); [375, 750); [750, 1,500); [1,500, 3,000); [3,000, 6,000); and [6,000, maximum]. eij reflects
barriers to trade arising from all other factors and is orthogonal to the regressors. The distance and
common border variables are obtained from the comprehensive geography database compiled by
CEPII.
To recover technology, I follow Waugh (2010) and use the estimated trade costs, k̂, and structural parameters, Ŝ, to compute the implied tradable good prices, p̂m , by rewriting equation 6 in
terms of Ŝ:
p̂mi = ( AB)
I
Âe
Ŝ j
j =1
k̂ij wij
1/q
!
q
From the obtained prices and the estimates Ŝi , I get the convolution of wages and technology,
log(wi
b/q
li ). Then, given the bilateral trade shares, Dij and the balanced trade condition in equa-
tion 8, I follow Alvarez and Lucas (2007) and and use the relationship between factor payments
19
Table 5: Simulated concentration level with exporter fixed effect
Gini
Model
Simulation
Data
Exports
Imports
0.99
0.89
0.98
0.91
Theil Exports (X)
Extensive Intensive
Total
Margin
Margin
4.83
59%
2.60
55%
3.32
41%
2.13
45%
Theil Imports (M)
Extensive Intensive
Total
Margin
Margin
8.15
0.84
34%
1.10
40%
4.73
1.61
66%
1.61
60%
2.45
2.71
and total revenue to calculate equilibrium wages.8
wi =
✓
(1
1
s f i ) Li
◆
I
 Lj wj
(1
j =1
s f j)
Fj
D ji w ji
!
where s f i is the labor share in the production of final goods
sfi =
(1
g(1 (1 b) Fi )
g) bFi + g(1 (1 b) Fi )
and Fi is the fraction of country i spending on tradable goods net of tariff expenses.
Fi =
I
 Dji w ji
j =1
The obtained equilibrium wages together with tradable good prices, determine the implied technology levels l̂ for each country given the structural estimates of the gravity equation.
Table 4 summarizes the regression outcome of the gravity equation. In terms of fitting bilateral
trade flows, I obtain an R2 of 0.82 slightly lower than the R2 of 0.83 reported by Waugh. The
obtained coefficients on trade costs are consistent with the gravity literature, where distance and
tariffs are an impediment to trade. The magnitudes of the coefficients reported in Table 4 are
similar to those in Eaton and Kortum (2002) and in Waugh (2010), which consider a similar sample
of countries without tariffs. The overall size of the trade costs in terms of percentage are similar to
those reported in Anderson and Van Wincoop (2004).
Next, I feed the model with the estimated trade costs and technology.9 Table 5 presents the
mean concentration levels for the simulated countries. The results show that the calibrated model
replicates the fact that countries are more specialized in exports than in imports on all margins.
Focusing on the obtained levels reveals that countries concentrate excessively on exports with re8 Given
9
factor endowments and optimal factor choice, the interest rates equals: ri = a/(1
See Table 7 at the end of the paper.
20
a)wi ( Li /Ki )
spect to the data. The simulated concentration levels are almost twice as high as the ones observed
in the data. Mean export (import) concentration on the extensive margin is 4.83 (0.84) compared
to 2.60 (1.10) in the data. This implies that in the simulated model countries export (import) 0.8
(43.2) percent of the product space compared to 7.4 (33.3) precent in the data.
Figure 4 plots the corresponding cross country pattern for simulated and empirically observed
concentration levels against the log of GDP. The model replicates the empirical pattern with export
concentration decreasing in market size. However, the simulated concentration levels on the extensive margin are too high, particularly for smaller economies. Countries specialize excessively
on the number of products exported. On the importing side, the calibrated model is unable to
replicate the L shape relationship between market size and concentration. The relationship does
not reveal any particular pattern. However, simulated countries tend to import more goods than
in the data. Turning the attention to the intensive margin, Figures 4(e) and 4(f), the results show
that, consistent with the data, the model predicts no relationship between concentration and market size. In sum, the calibrated model is able to replicate the qualitative pattern for exports but
produces relatively high levels of concentration compared to the data, particularly on the extensive margin.
A potential explanation for the excessive concentration in exports lies in the underlying productivity distribution. While the model reproduces the bilateral trade volumes, it fails to capture
the underlying distribution of trade volume across products. To shed light on why countries trade
too few products, I follow Haveman and Hummels (2004) and plot the empirical and the simulated
density of the number of exporters and importers per product.10 Figure 5 shows the results. In
the case of exports, simulated countries export their goods to too many destinations. The assumed
productivity distribution generates such efficient producers that even firms facing high trade costs
can sell their products to many destinations in the world. As a consequence, the number of exporting countries per product is small. In the data (in blue) more than a third of the products
are exported by 25 or more countries. In the simulation (in red) no product is exported by more
than 25 countries. Turning the attention to imports, Figure 5(b) shows that, contrary to exports,
the simulated distribution of the number of countries importing a product is closely related to the
empirical one.
5.1
Discussion of results
There are several potential reasons why the model is not able to reproduce the cross country pattern of import concentration on the extensive margin. Note that the model implies that expenditure shares equal to product shares in the tradable sector. The product share, pi , is defined as
10 To
get the empirical distribution of the number of exporters and importers per product, I count for each HS code the
number of countries that net export or net import the product. Similarly, the model implied distribution represents the
number of exporters and importers for each simulated product.
21
the number of products imported, Ni,M , divided by the total number of potential goods, N, i.e.
number of HS codes :
pi =
Ni,M
N
(16)
and the expenditure share, mi , equals the total value of imports, Mi , divided by domestic
absorption, Absi .
mi =
Mi
Absi
(17)
Figure 6 plots the empirical relationship between the two shares. The red line marks the 45
degree line where the two are equal. Notice that countries below the 45 degree import a lot of
goods and spend relative little on those goods, whereas countries above the 45 degree line import
few goods and spend a lot on them.
One potential reason of why countries are not aligned along the 45 degree line can be that
countries differ in the number of intermediate goods used in production. When calculating the
share of goods imported, I divide the total number of net products imported by the total number
of HS codes, which is common to all countries. If countries do not make use of all tradable goods
(for example they do not have the underlying technology to use a particular intermediate good),
then the calculated import product share for these countries is downward biased. Ethier (1982)
argues that larger economies use a higher number of intermediate goods because of increasing
returns to scale in the production process of the final good.
To shed light on the potential role of market size on the number of intermediate products in the
economy, we impose equality between product shares and expenditures shares, (pi = mi ). Given
this assumption, we can rewrite this equation as:
Mi
Absi
=
Ni,M
N
(18)
implying that the average per product import expenditure equals the average per product
tradable expenditure. Since the number of tradable goods is the same for all countries, we expect
that the elasticity of the average per product import expenditure with respect to absorption is 1.
Figure 7 plots the relationship. Note that the figure reveals a strong positive correlation with a
R2 = 0.84 and an elasticity of 0.6, significantly different from 1. Ethier’s argument that larger
economies have a higher degree of specialization and use a larger number of intermediate inputs
in the production of tradable goods can explain why the elasticity is below 1. In this case, the
number of tradable goods would be country specific and increases with the size of the tradable
sector.
Non-homothetic preferences may represent an alternative explanation for the fact that some
22
countries spend, on average, relatively more on few imported goods. Note that according to
equation 18, the ratio of the per product import expenditure with respect to per product tradable
expenditure should be one. This result relies on the assumption of homothetic preferences. Figure
8 plots the log of the ratio against the log of GDP per capita. The figure shows a negative correlation of -0.67 with a R2 = 0.23. This evidence is consistent with non-homothetic preferences, where
poorer countries spend relatively more per imported good compared to rich ones.
6
6.1
Robustness
Alternative classification schemes
This part addresses concerns on the robustness of the empirical observed concentration indexes.
In particular, the level of disaggregation as well as the classification scheme chosen may affect the
empirical concentration measures and the decomposition of the intensive and extensive margin.
For this reason, I re-calculated the concentration indexes on all margins by defining a product
as the equivalent of (1) a 4 digit SITC code and (2) a 6-digit NAICS code instead of a 6 digit HS
code. The advantage of the NAICS and SITC classification system is that products are grouped
according to economic functions such as their material and physical properties rather than for
tariff purposes as in the HS system. Table 6 reports the calculated concentration indexes based
on SITC and NAICS together with the correlation with respect to HS based based concentration
indexes. The qualitative estimates all classification are very similar. Exports are more concentrated
than imports. Concentration is driven by the extensive margin for exports and on the intensive
margin for imports. In terms of cross country evidence, larger countries import and export more
goods. Strikingly, the L pattern of the extensive margin also appears when using the SITC and
the NAICS classification. Differences between the various classification schemes appear in the
levels of import concentration. The reason is that the total number of 4 digit SITC codes is 642
and of NAICS codes 460, significantly lower than the 4529 HS codes. However, overall the high
correlation across the different classification highlight the level of generality the results apply.
6.2
Intra-industry trade
In this paragraph I want to address the discrepancy of the product space between the data and
the model caused by intra-industry trade. In the main part of the paper I establish correspondence between the model and the data by netting out within product trade. This approach leaves
valuable information unused and may bias the results. In an alternative approach, I deal with
intra-industry trade by developing a “measurement device” that enables the model to characterize intra and inter industry trade. The basic idea is that in reality the true state of the world is
indeed Ricardian, i.e. varieties are in fact products, but the data are not sufficiently disaggregated
to capture the true product level. Instead, these “Ricardian products” are aggregated into sectors
23
Table 6: Mean concentration indexes for gross trade flows based on the Armington assumption: 160 countries
Gini
Mean index
(HS 6 digit)
% share of overall
concentration
Exports
Imports
0.98
0.9
Theil Exports (X)
Extensive Intensive
Total
Margin
Margin
1.81
2.59
41%
59%
4.40
Theil Imports (M)
Extensive Intensive
Total
Margin
Margin
3.53
2.78
56%
44%
6.31
according to a classification scheme, i.e. HS codes. The suggested procedure converts the measurement of product units in the model to product units in the data and allows to examine gross
trade flows. Because the classification scheme is unobserved, I assume that varieties are randomly
assigned to an HS code following a Poisson process. Using the structure of the model, I can then
estimate the Poisson parameter and characterize the “measurement device”. I obtain a value of
0.94 for the Poisson parameter implying that, on average, one “Ricardian product” comprises an
HS product category. Based on this result, I group simulated Ricardian products randomly into
artificial HS codes and calculate the implied concentration indexes. The results, presented in detail
in the appendix, show that this approach leads to similar results as the net trade flow approach.
6.3
Implications of alternative trade models
Finally, I want to relate my analysis to alternative trade models, in particular, to monopolistic
competition models based on Krugman (1980) and Armington models based on Anderson and
Van Wincoop (2003). The key difference with respect to the Ricardian model is that in both types
of models tradable goods are differentiated by location of production. Applying this definition
of the product space to the data implies that each country is the sole producer/exporter of an
HS codes and demands all country product combinations. Hence, the number of potential goods
exported is 4529 and the number of potential goods imported is 4529 times 159 trading partners.
Table 6 presents the corresponding concentration indexes. The results shows that, conversely
to the above results, countries are more specialized in imports than in exports and the extensive
margin drives the import concentration. The reason is that a country imports only 3 percent of
the products its demands, which equals 4529 products times 159 countries. This implies that the
empirical implications used to evaluate a model depend on the definition of the product space,
i.e. differentiated versus homogenous goods. While it is certainly possible to generate the results
of Table 6 within a model based on the Armington assumption, the underlying mechanism to
generate speculation will be very different.11 In this paper, the analysis is based on the assumption
11 For
example by introducing fixed costs of trade (see Romer (1994)) or declining marginal utility of varieties (see
24
that foreign varieties are perfect substitutes to domestic ones consistent with the Ricardian model.
One motivating observation is that the Grubel and Lloyd (1975) index of 0.19 indicates that the
majority of the trade flows is inter-industry (81 percent) rather than intra-industry. However, I
cannot reject these alternative hypotheses for the observed concentration patterns and would like
to pursue them in future research.
7
Conclusion
I have argued that export and import concentration in combination with a decomposition into
an extensive and intensive product margin concentration measure provide new quantitative and
qualitative evidence on specialization patterns in world trade. Based on detailed trade data, the
calculations show that exports are more concentrated than imports on all margins and specialization is dominated by the extensive product margin for exports and by the intensive product
margin for imports. The extensive product margin explains the gap between export and import
concentration and drives specialization differences across countries. Larger economies diversify
more because they export and import more products. Furthermore, I show that the Eaton Kortum model is consistent with the observed patterns and partly replicates the stylized facts as
well as the cross-country differences qualitatively and quantitatively. Overall, my results stress
the importance of the role that geography and absolute as well as comparative advantage play in
determining the pattern of specialization.
By looking through the lenses of export and import concentration, this paper analyses how
openness to trade changes the production structure of an economy and how these changes relate
to income. My results show that the relationship between income and concentration is primarily
driven at the extensive margin. This relationship has important macroeconomic policy implications. Specialization increases a country’s exposure to shocks specific to the sectors in which
the economy concentrates. As a result, the likelihood that product specific shocks have aggregate effects in terms of output volatility and/or a negative impact on the terms of trade increases
with openness. Diversifying along the extensive margin reduces such risks whereas specialization
along the intensive margin by exporting industries that have already a comparative advantage to
more destinations intensifies the risk. Analyzing this question is an avenue for future research.
Ottaviano et al. (2002))
25
Figures
Gini index − HS 6 digit
Total Theil index − HS 6 digit
Mean of the index from 1992−2009
Mean of the index from 1992−2009
6
4
2
Export Concentration
.95
.9
.85
ITA
GER
NGA
TCD
YEM
OMN
GAB
COG
IRN
KWT
GNB
SAU
QAT MLI
COM
ARE
BFA WSM
BWA AZEBDI
RWA
KIR
BMU
BEN
SDN MWI
GIN
STP
CAF
VEN DZA ZMB MRT
NER
VUT
GMB
CUB
JAM
ECUSYR
MOZ
PNG
ETH
UGA
SLEBHS
ATG
CMR
GUY
CPV
ERI
GHA
PRY MNG
NOR TTO
TGO ARM
BHR
KAZ
DJI
BOL
ISL
BLZ MLT
BRB
KGZ
TZA
MUS
CRI
COL
ZWE
FJI GEO
SEN
NIC
KHM
RUS
PER
PAN
KEN
SWZ
CHL
MDGNPL
JOR
HND
EGY
SLV
LBN
LVA
GTM
BGD
CYPPHL
ALB
VNM
AUS
DOM
IRL
ARG
LTU MDA
BIH
ISR
MEX URY
AFG
MYSBLR
CAN
NZL
MKD
TUN
MAR
ZAF
GRC
LKA
EST
PAK
BRA
FIN
UKR
SRB
HRV
SVK KOR
PRT
HUN
IDN
ROM
BGR
DNK
SWE
THA
ESP
SVN
IND
TUR
POL
CHE
FRA
GBR
CZE
BEL
JPN
AUT NLD
USA
CHN
GER
ITA
0
.75
.8
Export Concentration
8
TCD
GAB
KWT
COM
YEM
COG
GNB
QAT
BDI
KIR
RWA
MLI
MRT
WSM
DZA
OMN
GIN
BMU
CAF
BEN
BWANGA
VUT
SDN
SAU ARE
STP
MWI
BFA
PNG
NER
CUB
GMB
ZMB
AZE
CPV
MOZ
BHS
CMR
GUY
IRN
ETH
JAM
ERI
GHA
VEN
ECU
UGA
TTO
PRY
MNG
TGO
BHR
BOL
ISL
DJI
SLE
ATG
BRB
ARMATG
ARM
SEN
KAZ
TZA
BLZ
NIC
MLT
MUS
KGZ
FJI
KHM
SYR
GEO
MDG
ZWE
HND
JOR
PER
NORCOL
CRI
SWZ
CHL
BGD
KEN
SLV
LBN
DOM
PAN
ALB
GTM
CYP
MDA NPL
URY
RUS
LVA
EGY
IRL
BIH
AUS
ARG
NZL
LTU
MARVNM
MKD
PHL
GRC
LKA BLR
AFG
PAK
MYS
MEX TUN
EST
SRB
CANHRV
ISR
ZAF
FIN
PRTROM
BRA UKR
SVK
HUN
BGR KOR
DNK
IDN
POLSVNSWE
TUR
THA
ESPCHE
AUT
CZE
BEL NLD
GBR
IND
FRA
JPN
USA
CHN
1
8
.75
.8
.85
.9
.95
1
0
2
4
Import Concentration
(a) Gini coefficient
8
(b) Theil index
Mean of the index from 1992−2009
6
Extensive Theil index − HS 6 digit
Mean of the index from 1992−2009
6
Intensive Theil index − HS 6 digit
4
0
PAN
KIR
TCDSTP
GNB
WSM ERI
CPV
BDI
VUT
RWA
BMU
BENGMB CAF
DJI
GAB
COG
MRT ATG
BFA
GIN
MWI
SDN
BWA BHS PNG
MLI
MOZNER
ETH
DZA
GUY
BRB
CUB
UGA
QAT
YEM
MNGARM
BLZ TGO
ZMB
KWT CMR
JAM
SEN AZE
NGA
GHA
PRY
NIC
SLE
BOL
KHM
TZA
BHR
FJI
ISL
TTO
OMN
ALB GEOKGZ
MLT
MDG
LBN
NPL
KAZ
MUS
CYP
AFG
JOR
SAU
SLV
VEN
ECU
HND
DOM SWZ
BIH
MDA
BGD
URY
GTM
ZWE
CRI
MKD
KEN
PAN
GRC
LVA
IRN
PER
HRV
LKA
SRB
CHL
MAR
SYR
LTU
TUN
ARE
BLR
EST
NOR
EGY
COL
NZL
VNM
ARG
PRT
ISR
IRL
PHL
ROM
AUS
PAK
UKR
FIN
MEX
SVK
SVN
CAN
BGR
HUN
DNK
POL
RUS
MYS
TUR
AUT
CHE
SWE
ESP
CZE
BRA
BEL
ZAF
GBR
IDN
THA
KOR
NLD
FRA
USA
IND
JPN
ITA
GER
CHN
2
4
2
Export Concentration
COM
IRN
ARE
NGA
OMN
SAU
YEM
SYR
KWT
VEN
NOR
QAT
ECU
MLI
AZE
RUS
GAB
COG
BWA
COL
ZMB
BFA
DZA
JAM
TCD
SLE KAZ
SDN
MWI
CRI
TTO
GIN
ZWE
CHL
PER
CMR
NER
CUB
EGY
GHA
PRY
KEN
AUS
MRT
MOZ
IRLMYS
BEN
VNM
BHR
MLT PHL
ETH
UGA
MEX
PNG
MUS
ARG
ISL BRA
ISRZAF
BDI
GUY
CAF
SWZ
CAN
BOL
MNG
BHS
LVA
BMU
KGZ
RWA
GNB WSM
TGO
KORIND
JOR
HND
NPL
GTM
ARM
TZA
LTU
IDN
GEO
FJISLV
BGD
BLR
GMB
ATG
MDG
NZL
PAK
FIN
THAJPN
HUN
FRA
UKR
BLZ
SVK
KHM
NIC
SEN
TUN
URY
MDA
LBN CYP
SWE
DOM
ESP
MAR
STPBIH
GBR
PRT
VUT
NLD
BRB
EST
CHE
DNK
TUR
CHN
MKD
LKA
BGR
GRC USA
CZE
BEL
ALB
POL
GER
ROM
COM
SVN
SRB
KIR
HRV
AFG
DJI AUT
ERI CPV
ITA
0
Export Concentration
6
Import Concentration
0
2
4
6
0
Import Concentration
2
4
6
Import Concentration
(c) Intensive margin
(d) Extensive margin
Figure 1: Average export versus import concentration for the period 1992 to 2009 for 160 countries
26
6
4
Import Concentration
USA
PLW
STPKIR
−10
−5
0
−10
Log of GDP relative to the US
USA
−5
0
Log of GDP relative to the US
R2=0.58
R2=0.41
(a) Overall concentration of exports
(b) Overall concentration of imports
Extensive Theil index − HS 6 digit
Mean of the log index from 1992−2009
Mean of the log index from 1992−2009
6
Extensive Theil index − HS 6 digit
6
COM
Import Concentration
USA
0
4
KIR
STP
VUT
AFG
GNB
ERIGNQTCD
COM
WSM
SLE
DJI CAF
ATG
HTI
BMU
LAO
IRQ
ZAR
ARM
RWA
GMB BDI
MRT
KGZ TKM
CPV
NER
COG
BEN
MNG
NPLUZB
MWI
SYC SUR
BFAGEO
TGO
MOZ
MLI
KHM AGO
FJI
AZE
ZWE BRB
BLZ
YEM
BIH
SWZ MDA
ALB
LBY
ZMB
UGA
QAT
CMR
SEN
SDN
BHR
SYR
KAZ
CIV
MDG
ETH
GERCHN
CUB
TZA
BLR
GHA
NIC
EST
ITA
PAK
IND JPN
LVA
KEN
MKD
KWT BGD
DOM
MLT
OMN
LTU
HND
MUS
BGR
JAM
VNM
TTO
UKR
PRY
PAN
LKA
URY
BOL
SLV
JOR
LBN
IRN
KOR
NGA
ISL CYP
FRA
SVN
NLD
THA
IDN
CRI
CZE
SVK
GBR
LUX
BRA
SWE
CHE
BEL
GTM
ZAF
AUT
POL
RUS
TUR
PHL
MYS
HUN
TUN
ROM
PER
ISR
ECU
ARE
ESP
DNK
MAR
EGY
FIN
SRB
ARG
COL
HRV
IRL
DZA
NZL
PRT
MEX
NOR
SGP
VEN
CHL
GRC
SAU AUSCAN
USA
0
2
PLW
4
GNQTCD
BDI
ERI RWA
AGO
IRQ
BMU
BEN
GMB
SYC
DJI CAF COG BFA
SDN
MRT MWI ZAR
ATG
LBY DZA
SURNERMOZ
TKM
MLI UGAETH
YEM CUB
ZMB
BRB
HTI JAMQAT KWT NGA
MNG
BLZ TGO
LAO
ARM
AZE
CMR
GHA
SENPRY
TZA
NIC
BOL
KGZ
ISL
SLE
TTO OMN
KHM
FJI
BHR
MDG
UZB
GEO
ALB
CIV
LBN ECUKAZ
MLT
CYP
NPL
JOR
VEN SAU
HND
MUS
SLV DOM
AFG
SWZ MDA BIH URY
ZWE
BGDGRC IRN
CRI GTM PER
KEN
CHL
MKDPAN
LVALTU
LKA
HRV
MARARE
SRB
SYR
EGY
TUN
NOR
EST LUX
BLR
NZLVNM
COL
ARGAUS
SGP
PRT
ISR
IRL
PHL
ROM
PAK
UKR
MEX
CAN
FIN
SVK HUN
BGR
SVN
POL
DNK
RUS
TUR ESP
MYS
AUT
BRA
ZAF
SWE
CZE
CHE
BEL
IDN
GBR
THA
NLD
KOR FRA
IND JPN
ITA
GERCHN
GNB
VUT
WSM
CPV
STP
2
PLW
KIR
Export Concentration
AFG
VUT
BMU
PAN
ATG
GNQ
GNB
COM
ERI
TCD
WSM
IRQ
ARM
SLE
CAF
HTI
DJI
MRTNER
KHM
BEN
GMB
SYC BDI
TKM
MLT
GEO
LAO
PHL
RWA
IND
KGZ
COG
BFA
TGO
CPV
ZAR
UZB
MWI
AGO
MLI
MOZ
NPL
YEM
AZE
QAT
MYS
SURMNG
OMN
BHR
CIV
SEN
ZWE
CYP
SDN
ZMB
CHN
CMR
LBY
SWZBRB
BLZ
ALB
UGA
FJI
ITA GERJPN
KWT
PRY
BIH
ISR
MDG
TZA
SGP CHE
KAZ
MDA
GHA
LUX
ETH
PAK
KEN
THA
LBN
TTO
SYR
IRL
NIC
BGD
NLDKOR
HND
ARE
DOM
JOR
DZA
BEL
JAM
GBR
EST
VNM
SLV
HUN
BOL
CUB
LKA
NGA
ISLMKD
MUS
LTU
UKR
BRA
LVA
SAUIRN
BLR
ZAF
SVK
EGY
ECU
RUS
NZL
CRI
IDNCAN
GRC
BGR
AUT
SWE
DNK
URY
NOR
MEX
CZE
FIN
TUN
AUS
GTM
COL
TUR
VEN
SVN
PER
ESP
FRA
PRT
MAR
ARG
CHL
SRB
POL
HRV
ROM
0
2
4
STP
AGO
GNQ
SUR
MOZ
MLIZAR
BMU
CAF
BDI
MRTNER ZMB JAM
VUT
COG
GMB
TCD
GNB
SLE
ERI RWA
CMR SDN
CPV
ATG
BEN
IRQ
GHA
KWT
BFA TTO
CUB
QATLBY
ARM
KGZ
UGA
ETH
MNG
ISLSENTKM
TGO
BOL
DZA
TZA
LAO
DJI
YEM
PRY
MLT
UZBECU PER
BHR
MWI
NGA
BLZ
CHL
NIC
HTI
ZWE BRB
MDG
NPL
AZE
KHM
KAZ
JOR
MUS
CIV
GEO
CRI
LBN
HND
SWZ
FJI
SGP SAUIRN
ALB
OMNDOM ARE
BGD
CYP
PHL ARG
VEN
MDA
URY
IRL
SLV
PAN
ISR
KENGTM
BIH
LVA
LUX
NZL
ZAF AUSCAN
MYS
MAR
MKD
AFG
GRC
MEX
TUN
BLR VNM
NOR
SYR
PAK
LKA
RUS
COL
EST LTU
FIN
UKR
SRB
EGY
HRV
PRT
SVK HUN
BRA
KOR
ROM
THA
SWE
DNK
BGR
TUR ESPFRA
SVN
POL
IND
CHE
IDN
CZE
GBR
BEL
AUT
JPN
NLD
GERCHN
ITA
COMWSM
SYC
2
6
KIR
PLW
0
Export Concentration
8
Total Theil index − HS 6 digit
Mean of the log index from 1992−2009
8
Total Theil index − HS 6 digit
Mean of the log index from 1992−2009
−10
−5
0
−10
Log of GDP relative to the US
R2=0.75
(d) Extensive margin of imports
Mean of the log index from 1992−2009
4
STP
KIR
PLW
BMU
PHL
MLT
IND
MYS
CYP
SGP
ATG
ISR
OMN
IRL
LUX
AFG
DZA
SAU
KHM
ARE
CHE
PRY
GRC
QAT
THA
CIV LBN KWT
BHR
BEL
ITA
NLD
NZLHUN
VUT
GBR
BGD
AUS
SYC
SEN
BEN
NOR
EGY
TTO
ARM
MEX
IRQ
BRA
ECU
CHN
VEN
KEN
ZAF
PAK
YEM
GEO
JOR
RUS
AGO
SDN
CAN
DNK
IRN
KOR
ISL
NGA
COL
FIN
SVK
NER
SLV
AUT
CMR
PRT
HND
BOL
GHA
TZA
LKA
TUN
CHL
SWE
AZE
JAM
CRI
MDGUGA
DOM
GTM
ZMB
IDNESP GERJPN
VNM
BFA
ARGTUR
TGO MUS
KAZ
PER
CZE
ETH
NIC
MRT
UKR
MAR
MLI
LBY
LTU
SRB
HRV
UZB
SWZ
ZWE
SYR
ALB
MOZ
HTI
URY
GMBBLZ
MKD
POL
SVN
EST
MWI
BGR
COG
LVA
TKM
GNQ
FRA
BDI
KGZ
CPV
NPL
CUBROM
MNG
CAF
BIH
RWA
BLR
MDA
SURBRB
COMWSM
LAOZAR
DJI FJI
SLE
ERI TCD
GNB
USA
0
0
1
USA
PAN
3
ZMB JAM
SUR MOZ
SLE
NERMLI
PER
SGP
CHL
TTOAGO
ZAR
SYC
PHL
ZAF
ZWE
KGZ
CMR ECUIRL
MRT MLT
ARE
ISRMYS
ARG
CAF
ISL GHA
CAN
CRI
IRN
UZB KWT
SWZ
ARMBHR
NPL
AUS
WSM BMU
COGMUS
KOR
JOR
QAT
RUS
MEX
BOL
IND
KAZ
TZA
HND
SAU
PRY
SENPAN
MNG
CIV
GMB
GNQ
VNM
BGD
NZL
FIN
BRA
PAK
LUX
CUB
HUN
FRA
MDG
THA ESP
LBNSDN
UKR
LVA
LAO
JPN
URY
KHM
TGO MDA
NICGEO
NGA
UGA
ATG
KEN
SWE
SVK
BLR
MAR
DOM
TUN
NOR
BDI
LBY
PRT
GTM
VEN
CYP
COL
BLZ
CHE
FJI
SLV
GBR
BIH AZE
TUR
IDN
MKD
DNK
EST
ETH
BFA
HTI
SYR
YEM
CZE
BEL
ALB
LTU
GRC
GERCHN
DZA
OMN
BGR
POL
VUT
SVN
EGY
LKA
ROM
NLD
BRB
TKM
SRB
AUT
HRV IRQ
BENAFG
KIR COM
MWI
ERI
TCD
ITA
CPV
PLW
DJI
RWA
GNB
STP
2
Import Concentration
5
Intensive Theil index − HS 6 digit
Mean of the log index from 1992−2009
5
Intensive Theil index − HS 6 digit
4
3
2
Export Concentration
0
R2=0.54
(c) Extensive margin of exports
1
−5
Log of GDP relative to the US
−10
−5
0
−10
Log of GDP relative to the US
−5
0
Log of GDP relative to the US
R2=0.01
R2=0.12
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 2: Average export and import concentration versus the log of average relative GDP with respect to the
United States for the period 1992 to 2009 for 160 countries.
27
Total Theil Index
Total Theil Index
Data versus simulation
Data versus simulation
12
8
Red − Simulated Data
Blue − Empirical Data
11
Import Concentration
Export Concentration
9
GNB
8
AGO
VUTBLZ
STP
GNQ
KIR COM
WSM
SYC ERI
SUR
PLW
MOZ
ZAR
MLI
KIR
BMU
NER
CAF
ZMB
BDI
PLW
MLI
COM
MRT MDA
MKD
MOZ
JAM
VUT
BEN
GMB COG
RWA
TCD
AZE
ATG
UGA
WSM
CPV
GNB
GMB
CIV
SLE
TKM
SDN
ARM
SYC
DJI ERI RWA
GEO
CMR
CPV
ATG
TTO
BEN
YEM
GHA
IRQ
KWT
BFA
BMU
CUB
ZWE
MRT
JAM
QAT
LBY
ARM
KGZ
STP
UGA
TZA
QAT
FJI
CAF
BDI
SLE
ETH
GHA
MNG
ISL
TUR RUS
LKA
SWZ
TUN
LBY
TGO
BOL
DZA
TZA
LAO
SEN
SVKPER
DJI SUR
TKM
DNK
YEM
NER
PRY
ECU
MLT
UZB
GNQ
COG
BHR
NOR
MWI
NGA
MNG
SGP
MLT
THA
BLZ
CHL
TCD
NIC
HTI
KAZ
BRB
ROM
NGA
BFA
PAK
LAO
KGZ
ZWE BRB
MUS
ALB
HTI
NIC
ZAR
MDG
NPL
AZE
KHM
KAZ
ISL
GRC
ZMB
JOR
SEN
CZE
MUS
CIV
AFG
GEO
EST
BHR
NPL
CRI
PAN
LBN
HND
SWZ
FJI
URY
IRN
BIH
LVA
SGP SAU
CMR
JOR
CYP
AGO
LUX
UZB
ARE
TTO
PRY
ALB
SLV
SVN
DOM
OMN
KEN
BOL
KWT
LTU
ETH
BGD
CYP
SDN
LBN
GER
HRV
DOM
VEN
PHLARG
SYR
CRI
BGR
MDA
BLR
IRQ
URY
IRL
GTM
SLV
PAN
IDN BRA
MAR
ISR
AUS
CUB
ECU
IRN
KEN
GTM
BIH
SRB
NZL
BEL
SAU
ARG
LVA
ARE
VEN
NLD
POL
MEX
LUX
FIN
CAN
PHL
UKR
VNM
PRT
BGD
CHE
NZL
ZAF AUS
SWE
ITA
PER
AUT
MYS
MAR
ESP
CHL
COL
ISR
KOR
FRA
IRL
MKD
EGY
CAN
VNM
AFG TUN
GBR
GRC
HUN
BLR
NOR
SYR
PAK MEX
LKA
RUS
COL
EST LTU
FIN
IND
UKR
SRB
EGY
HRV
PRT
SVKHUN
BRA JPN
KOR
THA
SWE
DNK
BGR ROM
TURESP
SVN
FRA
POL
IND
CHN
CHE
IDN
CZE
GBRJPN
BEL
AUT
NLD
USA
GERCHNUSA
ITA
7
6
5
4
3
2
1 −6
10
−4
10
−2
10
Red − Simulated Data
Blue − Empirical Data
7
10
6
AFG
VUT
BMU
PAN
PLW
ATG
KIR
GNQ
STP COM
GNB
WSM
VUT
GNB
KIR COM
STP
ATG
ERI
TCD
CPV
GMB
BLZ
SYC
WSM
IRQ
DJI
CAF
BMU
ARM
BDI
ZWE
SLE
HTI
GNQ
ERI
MWI
BRB
DJICAF
FJI
MUS
NER
MRT
KHM
BEN
GMB
SUR
SYCBDI
COG
MDA
RWA
TKM
TGO
MNG
SEN
SWZ
MLT
GEO
LAO
PHL
RWA
IND
HTI
ALB
TCD
ZMB
GEO
KGZ
COG
BFA
MLT
BEN
TGO
CPV
ZAR
LAO
TTO
MOZ
UZB
MWI
BFA
AGO
MLI
ARM
AFG
ISL
MOZ
NPL
CYP
MNG
NIC
YEM
MDG
AZE
QAT
KHM
MYS
BIH
JOR
SUR MKD
EST
OMN
BHR
CIV
SEN
UZB
ZWE
PRY
NPL
CYP
USA
BLR
HND
UGA
SDN
ZMB
GHA
AZE
URY
CIV
CMR
BOL
PAN
AGO
TZA
LVA
JAM
LBY
SVN
KWT
SVK
SWZ
YEM
ETH
BLZ
ALB
UGA
KEN
SDN
FJI
ITAGERCHN
KWT
LUX
CRI
LBN
PRY
SLV
LTU
BIH
QAT
ISR
MDG
BRB
TZA
SGP
OMN
KAZ
SYR
MDA
GHA
LUX
ETH
DOM
PAK
SRB
LKA
KEN
JPN
BGR
THA
LBN
SYR
LBY
IRL
TUN
NIC TTO
ECU
GTM
BGD
CHE
HRV
NLD
HND
ARE
KAZ
HUN
DOM
IRQ
JOR
DZA
BEL
MAR
JAM
PER
PHL
GBR
EST
VNM
SLV
HUN
IRN
BOL
CUB
KOR
COL
LKA
NGA
IRL
ISL
DZA
MUS
LTU
UKR
BRA
VNM
ISR
CHL
MKD
LVA
SAU
BLR
NGA
ZAF
ARE
UKR
SVK
EGY
DNK
ECU
RUS
FIN
ROM
NZL
CZE
CRI
IDN
GRC
BGR
SGP
MYS
AUT
DNK
SWE
VEN
SAU
USA
CHE
URY
NOR
PAK
PRT
CZE
MEX
FIN
TUN
AUS
GRC
GTM
SWE
COL
TUR
VEN
BRA
BEL
CAN
SVN
JPN
ZAF
PER
ESP
RUS
FRA
PRT
MAR
KOR
IRN
NLD
AUS
CHL
GBR
THA
SRB
TUR
POL
ARG
GERCHN
HRV
CAN
ROMARG
IND
IDN
ESP
FRA
ITA
MEX
5
PLW
4
3
2
1 −6
10
0
10
Log of GDP relative to the US
−4
(a) Overall concentration of exports
0
10
Extensive Theil Index
Data versus simulation
Data versus simulation
6
6
Red − Simulated Data
Blue − Empirical Data
STP COM
PLW
PLW
KIR
STP
4
3
2
1
0 −6
10
Red − Simulated Data
Blue − Empirical Data
5
GNQ
GNB
TCD
COM
VUT BDI RWA
WSM
AGO
CPV ERI
GNB BMU
IRQ
BEN
GMB
SYC
CAF
DJI
WSM
COG
ZAR
SDN
BFA
VUT
MWI
ATG
CPV MRT
ETHLBY
DJI SUR
GMB
MOZTKM
SYC
NER
DZA
MLI
UGA
CUB
YEM
ZWE
BMU
CAF
BLZ
BDI
SUR
MRT
ERI
SLE
SWZ
FJI
ZMB QAT
BRB
HTI
MNG
BLZTGO
LAO
JAM
ARM
COG
AZE KWTNGA
GNQ
NER
CMR
GHA
SEN
MNG
TZA
MWI
RWA
NIC
BRB
BEN
TCD
BOL
PRY
ARM
LAO
MLT
BFA
MOZ
MDA
MLI
KGZ
NIC
HTI
MUS
ALB
KGZ
ZAR
ISL
AFG
SEN
SLE
TTO
MDG
KHM
FJI
BHR
MKD
MDG
GEO
ZMB
OMN
EST
UZB
ALB
GEO
BHR
BIH
TKM
UGA
NPL
KAZ
CIV
GHA
TTO
LBN
CMR
LVA
PRY
PAN
JOR
JAM
MLT CYP
HND
TZA
UZB
YEM
CYP
AGO
ECU VENSAU
URY
BOL
LUX
AZE
NPL
JOR
LTU
ETH
HND
QAT
MUS
OMN
SVN
SLV
KEN
LBN
AFG
DOM
CRI
SYR
LBY
KWT
TUN
SDN
BGR
DOM
IRQ
LKA
HRV
BLR
GTM
SVK
CUB
SWZMDABIH
ZWE
MAR
BGD IRN
SRB
URY
ECU
CRI
KEN
KAZ
NZL
VNM
CHL
GRC
MKD
BGD
CZE
ROM
PER
PHL
NGA
ZAF
PRT
ARE
BEL
PAK
ARG
DNK
FIN
UKR
SGP
SWE
NOR
IRL
PAN
THA
LVAHRV
LKA
CHE
VEN
AUT
PER
CHL
MYS
MAR
SRB
ISR
DZA
EGY
COL
POL
SAU
SYR
HUN
IRN
TUR
NLD
GRC
IDN
MEX
LTU
EGY
BRA
TUN
ARE
AUS
NOR
RUS
GBR
ESTLUX
ITA
BLR
NZL
ESP
COL
FRA
KOR
GER
CAN
IND
ARG
VNM
AUS
SGP
PRT
ISR
IRL
PHL
ROM
PAK CAN
UKR
MEX
FIN
SVKHUN
BGR
SVN
POL
DNK
RUS JPN
CHN
TURESP
MYS
AUT
BRA
ZAF
SWE
CZE
CHE
BEL
IDN GBR
THA
NLD
KOR
FRA
USA
IND
ITA
JPN
GER
CHNUSA
−4
10
−2
10
Import Concentration
5
Export Concentration
10
(b) Overall concentration of imports
Extensive Theil Index
PLW
4
KIR
STP
3
2
1
0 −6
10
0
10
Log of GDP relative to the US
VUT
GNB
−4
8
Red − Simulated Data
Blue − Empirical Data
7
Import Concentration
5
0 −6
10
GNB BLZ
MKD AZE
MLI
MDA
TUR RUS
VUT ERI MOZUGA
DZA
CIV
ZMB
BEN
TKM
SURRWA
DNK
MOZ
JAM
YEM
MLI
NORTHA
SLE
NER
GEO
LKA
SGP
QAT
PER
TUN
AGO
JAM
SVK
LBY
SGP
CHL
TZA
PAK
NGA
TTO
ROM
GRC
KAZ
ARM
GHA
ZAR
SYC
PHL
ZAF
ZWE
CZE
KGZ
IRL
CMR ECU
MYS
MRTMLT
ARE
ISR
ARG CAN GER
CAF
ISL GHA
KWT
CRI
IRN
UZB
BMU
SWZ
ARM
BHR
GMB
NPL
AUS
WSM
COG
MUS
KOR
JOR
IDN
QAT KAZ SAU
RUS
MEX
BRA
BOL
SYC
ATG
IRN
CPV
IND
PAN
HND
TZA
MRT
PRY
SEN
DJI
BMU
MNG
CIV
GMB
GNQ
KOR
VNM
FJI
NZL
BGD
DOM
ZWE
CAN
NLD
FIN
BRA
PAK
SAU
LUX
MEX
CUB
ITA
MWI
HUN
MLT
URY
SLE
FRA
ESP
PAN
MDG
NER
THA
KWT
BDI
TGO
NPL
POL
LBN
WSM
MNG
UKR
MUS
LVA
ALB
LAO
JPN
VEN
URY
SUR
BEL
GNQ
HND
ZMB
BFA
HRV
HTI
ARG
KHM
SVN
KGZ
GEO
COG
KEN
BHR
SLV
LUX
TCD
SDN
ZAR
SWZ
ARE
TGO
EST
ESP
LVA
ISL
AGO
NIC
GTM
MDA
NGA
LAO
BGR
GBR
UGA
UZB
BRB
IRQ
NZL
MDG
MYS
ATG CAF
FIN
KEN
SWE
JOR
SVK
SEN
CMR
BLR
CHE
ECU
LTU
SYR
UKR
LBN
CUB
MAR
OMN
PER
DOM
COL
AFG
AUT
TUN
BOL
ETH
CRI
ISR
PHL
NOR
PRT
BDI
SRB
CHL
EGY
SWE
BIH
LBY
ZAF TUR
TTO
BGD
INDJPN
PRY
GTM
VNM
VEN
IRL
CYP
HUN
COL
BLZ
CHE
FJI
SLV
BIH
IDN GBR
KIR COM
MKD
DNK
EST
ETH
BFA
STP
HTI
SYR
YEM
CZE
BEL
ALB
LTU
GRC
CHNUSA
GERCHN
AZE
DZA
PLW
OMN
BGR
VUT
SVN
POL
EGY
LKA
NLD
BRB
TKM
SRB ROM
AUT
HRV
BENAFG
KIR COM
IRQ
MWI
ERI
TCD
ITA
CPV
PLW
RWA
GNBDJI
STP
−4
10
−2
10
Red − Simulated Data
Blue − Empirical Data
7
6
1
0
10
Data versus simulation
8
2
10
Intensive Theil Index
Data versus simulation
3
−2
10
(d) Extensive margin of imports
Intensive Theil Index
4
AFG
ERI TCD
GNQ
COM
GNB
PLW
KIR COM
WSM
STP
VUT SLE
ATG
DJI
CPV
GMB
ATG
CAF
SYC
DJI
BLZ
CAF
ZWE
BMU
HTI
BDI
SLE LAO
IRQ
ZAR
ARM
RWA
GMB ERI
FJI
GNQ
MRT
COG
SUR
NER
TGO
KGZ TKM
CPV BDI
SWZ
NER
COG
MNG
RWA
BRB
TCD
MWI
KGZ
BEN
MNG
MDA
NPL
MWI
LAO
SYC SUR
BFA
TGO
GEO
MLT
MLI
HTI
MOZ
MLI
BFA
ARM
KHM UZB
ALB
FJI MUS
NIC
MDG
ISL
ZMB
ZAR
AFG
MKD
SEN
GEO
AGO
BIH
AZE
KHM
ZWE MDA
BLZ
BHR
TTO
EST
YEM
PRY
BIH
TKM
JOR
HND
NPL
GHA
CYP
PAN
BRBALB
UGA
SWZ
CIV
AGO
CMR
AZE
LBY
ZMB
LVA
TZA
ETH
JAM
BOL
UGA
URY
KEN
QAT
CMR
SEN
SLV
SDN
LUX
YEM
UZB
CRI
LTU
LBN
BHR
SYR
KAZ
CIV
MDG
QAT
ETH
SDN
OMN
GERCHN
CUB
TZA
BLR
LKA
BGR
GHA
SVN
NIC
EST
DOM
LBY
GTM
TUN
HRV
ITA
BLR
PAK
LVA
INDJPN
ECU
KEN
MKD
IRQ
KWT
DOM
MLT
SRB
LTU
OMN
KAZ
HND
MUS
BGR
JAM
VNM
CUB
UKR
TTO
PRY
HUN
SVK
ISR
KWT
USA
PAN
PER
LKA
URY
IRL
BOL
SLV
JOR
MAR
BGD
DZA
LBN
IRN
KOR
NGA
ISLCYP
FRA
SVN
NZL
NLD
THA
VNM
CHL
IDN
FIN
CRI
NGA
ARE
UKR
CZE
USA
SVK
GBR
LUX
ROM
MYS
BRA
DNK
SWE
CHE
BEL
ZAF
GTM
COL
SGP
AUT
POL
RUS
PHL
EGY
TUR
PRT
VEN
MYS
NOR
HUN
TUN
ROM
ISR
PER
ECU
ARE
ESP
GRC
DNK
AUT
PAK
MAR
EGY
SWE
FIN
SRB
ZAF
ARG
COL
HRV
JPN
IRL
BEL
DZA
IRN
CHN
THA
NZL
NLD
PRT
MEX
POL
CAN
NOR
TUR
ARG
IDN
KOR
SGP
VEN
CHL
SAU
AUS
GER
IND
GRC
SAU CAN
ESP
FRA
GBR
ITA
MEX
RUS
BRA
Log of GDP relative to the US
(c) Extensive margin of exports
Export Concentration
−2
10
Log of GDP relative to the US
6
5
3
2
1
0 −6
10
0
10
Log of GDP relative to the US
PAN
4
BMU
PHL
MLT
IND
MYS
CYP
SGP
ATG
ISR
OMN
USA
TKM
IRL
LUX
KWT
BLR
SVK
SEN
AFG
DZA
KHM
SAU
MUS
ARE
CHE
PRY
GRC
QAT
BRA
THA
CIVLBN
BHR
MWI
GEO
KWT
ZMB
RUS
BEL
UZB
TTO
HUN
CYP
ITA
BRB
NLD
ALB
SAU
NZL
PHL
COL
GBR
PLW VUT
SVN
BGD
AUS
SYC
SEN
BEN
NOR
MDA
EGY
TTO
ARM
SRB
HTI
IRQ
MEX
ZAR
BRA
ECU
VEN
CHN
GNQ
ZAF
KEN
PAK
YEM
GEO
JOR
RUS
MAR
AGO
SDN
CAN
DNK
GER
IRN
HUN
KOR
ISL
NGA
AUT
COL
EGY
FIN
NER
SVK
UKR
JPN
SGP
AUS
BEL
PER
ARE
VEN
SLV
ROM
NOR
ECU
CMR
PRT
CHE
HND
BOL
URY
CZE
GRC
GHA
DZA
VNM
AFG
TZA
YEM
KAZ
TUN
LKA
QAT
IRL
CAN
CHL
AZE
SWE
ZAF
CHNUSA
MYS
MEX
ITA
FRA
JAM
LBN
GER
BLZ
CUB
RWA
LBY
NLD
CPV
OMN
CRI
LTU
MDG
IND
BDI
VUT
MLT
NZL
BMU
IRN
GTM
DOM
ZMB
ISL
LUX
TUR
IDN
HRV
IRQ
BFA
VNM
POL
KHM
SYR
ARG
TGO
GMB
ESP
NER
ATG
SYC
TZA
KEN
BGR
THA
KAZ
PER
MOZ
CZE
CMR
ETH
DJI
NPL
MNG
NIC
MRT
UGA
FJI
SUR
EST
LVA
CIV
UKR
ERI
BIH
ARM
MAR
SWZ
SLV
TCD
MKD
ISR
AGO
MUS
WSM
SLE
CAF
MLI
HND
LBY
MDG
BHR
BEN
GNB
PRY
COG
GHA
LTU
TGO
SRB
KGZ
HRV
LAO
PAN
UZB
ZWE
SWZ
SYR
ALB
MOZ
HTI
KIR COM
URY
STP
GMB
MKD
POL
SVN
EST
ROM
MWI
BGR
COG
TKM
LVA
GNQ
FRA
BDI
KGZ
CPV
NPL
BRB
CUB
MNG
BLZ
CAF
BIH
RWA
BLR
MDA
SUR
COM
LAO
WSM
FJI
DJI
ZAR
STP
SLE
ERI TCD
KIR GNB
PLW
−4
10
−2
10
0
10
Log of GDP relative to the US
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 3: Simulated (in red) and empirical observed (in blue) export and import concentration versus GDP
across 160 countries. The simulation uses parameterized trade costs to match the data using a country specific
export cost.
28
Total Theil Index
Total Theil Index
Data versus simulation
Data versus simulation
20
ATG
18
8
Red − Simulated Data
Blue − Empirical Data
Red − Simulated Data
Blue − Empirical Data
7
PLW
KIR
14
Import Concentration
Export Concentration
16
PLW
STP
BMU
GNB
CPV
ERI
BDI SYC
TCD
SEN UZB
WSM
COM
SLE GEO
AZE
MNG
IRQ
CAF SDNNGA
CUB
VUT
ARM BIH
MLI
NER
TKM
ALB NPL
MRT
MUSMLT
BRB
FJI
AFG
MKD
ZARSUR
DOM
DJI
MWI
LBY
DZA ECU
ETH
JAM
COG
KGZ
GNQ
CYP CMR
COL
VEN
TZA
RWA TGO
MDG
OMN
ISL
SLV
TTO HRV
BHR
GHA
HTI
BLZ
HND
QATCIV
BFA
BOL
NICMDA
PRY SYR
NZL
IRN
SAU
GMB
AGOLAO
SRB
BEN
KHM LKALTU
YEM
LBN
PAN
MOZ
KAZ
KEN
ZWE LVA
ARE
EST
AGO
MAR
ZMB
TUN PER
SVK
GNQ
SWZ
COM
JOR
PHL ZAF
UGA
WSM
SYC
SUR
MOZ
CRI
ZAR
MLI
KIR
BMU
NER ZMB
CAF
LUX
BDI KWT
BLRROM AUS
PLW
GTM JAMBGD VNM
VUT
COG
GMB MRT
ISR
TCD
GNB
EGY
SVN UKR
BGR
SDN
ERI RWA
CMR
CPV SLE
ATG
TTO
MEX BRA
GRC
HUN
BEN
GHA
IRQ
KWT
BFA
CUB
QAT
LBY
ARM
KGZ
STP
UGA
TUR
URY
CHL
ETH
MNG
ISL
IND
TGO
NOR
BOL
ITA
DZA
TZA
LAO
SEN
DJI
TKM
YEM
PRY
ECU
MLT
UZB
BHR
MWI
NGA
PAK
POL
PER
ARG
BLZ
CHL
NIC
HTI
CZE
ZWE BRB
MDG
NPL
SWE
AZE
KHM
KAZ
JOR
CIVCRI
GEO
RUS
AUT
SAU
LBN
HND
THA
SWZ MUS
FJI
IRN
SGP
DNK
PRT
ARE
ALB
OMN
BEL
BGD
CYP
VEN
PHL
DOM
ARG
MDA
IDN
FINCHE
URY
IRL
SLV
PAN
ISR
KEN
GTM
IRL
BIH
NLD KOR
LVA
AUS
LUX
CAN
NZL
ZAF
MYS
MAR
MKD
VNM
AFG
CHN
GRC
ESP
MEX
TUN
BLR
NOR
SYR
LKA
PAK
RUS
COL
EST LTU
FIN
UKR
SRB
EGY KOR
HRV
PRT
SVKHUN
JPN
BRAGBR
ESP
THA
SWE
DNK
BGR ROM
TUR
CAN
SVN
FRA
POL
IND
CHE
IDN GBRJPN
CZE
BEL
SGP
AUT
FRA
GER
MYS
NLD
GERCHNUSA
ITA
USA
12
10
8
6
4
2
−6
−4
10
−2
10
10
6
2
1
10
−6
−4
10
Extensive Theil Index
Data versus simulation
BMU
PLW
VUTCPV
WSM
AFG
TCD
ERI
GNB
DJI
8
6
4
2
0
−6
MNG
SYC
SUR
BRB
SLE TKM
HTI
ISL
NPL
BIH
RWA
GMB
BDI BLZ
ALB
BFA
MLI
MWI
KGZ
LAOBOL
DZA
GNQ
FJI
SDN
BEN NER YEM
CUB
PRY
MDA
MLT
ARM
LBY
GEO
CAFAGO
MDG
LKA
AZE
MRT
ZAR
MKD
LUX
BHR
TGO COM
CYP
KHM
LBN
MUS
UZBSYR
JAM
PLW
KIRCOG
UGA
KWT
TTO
GNQ
GNB MOZ
ECU HRV
TCD
BGD
VUT
TZA
LVA SRB
NICBDI
STP ETHWSM
SWZ
ERI RWA
AGO
JOR
CPV
LTU
EST
MARIRQ
PHL
ZMB
BMU
CMR
SVN
NGA
BEN
GMB
SYC
NZL
CAF
VEN
SLV
DJI
BLR
DOM
KEN
OMN
COG
KAZ
URY
ZAR
SDN
BFA
SEN
TUN
MRT
MWI
ATG
PAN
LBY
IRN
SUR
ETH
GTMTKM
HND
MOZ
PER
BGR
SVK
EGY
NER
DZA
QAT
MLI
UGA
CUB
YEM
ZWE
ZMB
TGO
HTI
BRB
QAT
MNG
BLZ
LAO
JAM
NGA DNK
ARM
KWT
GHA
AZE
CMR
GHA
COL
SEN
TZA
CIV
VNM
NIC
BOL
PRY
GRC
CHL
ISL
CZE
HUN
SLE
TTO
KHM
FJI KGZ
BHR
ROM
MDG
NOR
OMN
UZB KAZ
GEO
ALB
AUSCHE
CRI
CIV
PRT
LBN
MLT
CYP
SAU
ECU VEN
NPL
JOR
HND
MUS
SLV
DOM
AFG
UKR
IRL
BEL
BIH
ZWE
SWZMDA
BGD
URY
CRIGTM
KEN
AUT NLD
POL
CHL
GRCIRN
MKD
PER
FIN
KOR
PAK
PAN
LVA
LKA
HRV
MAR
SRB
SYR
SAU
LTU
LUX
EGY
TUN
ARE
NOR
EST
NZL
BLR
COL
ISR
TUR
ARG
VNM
ARE
AUS
SGP
PRT
ISR
RUS CAN
IRL
PHL
ROM
ZAF
IDN
PAK
UKR
MEX
CAN
FIN
SVK
BGR
HUN
SWE
SVN
POL
DNK
RUS
TUR
MYS
ARG
AUT
BRA
ZAF
ESP
IND
SWE
CZE
CHE
BEL
IDN
GBR FRA JPN
THA
SGP
MEX
BRA
NLD
KOR
FRA
ESP
USA
INDITA
THA
JPN
CHN
GBR
MYS ITAGER
GER
CHN
USA
−4
10
Red − Simulated Data
Blue − Empirical Data
PLW
5
Import Concentration
Export Concentration
6
Red − Simulated Data
Blue − Empirical Data
PLW
STP
COM
IRQ
0
10
Extensive Theil Index
Data versus simulation
10
10
(b) Overall concentration of imports
14
KIR
−2
10
Log of GDP relative to the US
(a) Overall concentration of exports
12
PAN
ATG
GNQ
GNB
COM
ERI TCD
WSM
IRQ
ARM
GNQCAF
SLE CMR
HTI
DJI MRT
BRA
SYR
IRN
NER
KHM
BEN
GMB
SYC
TKM PER PHL AUS
MLT
GEO
LAO
RWA
IND
MWI TKM BDI COG
ZAF
KGZ
JPN
BFA
TGO
DOMVEN
CPV
UZB
ZAR
TCD
UZB
MWI
AGO
MLI
BFA FJI
BHR
ERI
MOZ
NPL
MNG
YEM
AZE
KWT
SLV
QATNZL
MYS
YEM
AGO
SVN
ISR
COL
TTO
SUR CIV
KAZ
OMN
BHR
CIV
SEN
RUS
AZE
KGZ
ROM
CHL
ZWE
ARG
SRB
CYP
USA
TUN
MDG BIH
SAU
SDN
ZMB
SDN
ZWEGTM
CHNCHN
BDI CAF
CMR
BLR
USA
ARM
LBY
PHL TUR
SWZ
BOL
KEN
BLZ
ALB
UGA
ITA
FJI
UGA
BGD
ETH
GER
KWT
RWA
DZA
PRY
BIH
ISR
LKA
MDG
ECU
IND
TZA
BRB
SWZ
SGP
NPL MDA TTO
EGY
HTI
GHA
LUX
LBY
ETH
PAK
KEN
JPN
HUN
HRV
THA
LBN
MOZ
GNBWSM
SYRKAZ
AUT
IRL
NIC
BEN SLE ALB
TZA
BGD
MUSMLT
CHE
NLD
SEN
HND
ARE
SUR
CRI
MAR
JOR
DOM
JOR
DZA
BEL
CPV MLI
GRC
FIN
JAM
UKR
GBR
EST
PAK
SLV
VNM
HUN
IRNKOR
BOL
QAT
CUB
LKA
NGA
ISL
OMN
BGR
KHMMUS
SYC
LTU
UKR
BRA
MKD
PRY
ARE
MKD
SAU
LVA
LAO
NGA
BLR
ZAF
SVK
NER
EGY
ECU
RUS KOR
FRA
NZL
CHE
POL
CRI
IDN
GRC
BGR
NICLBN
AUT
SWE
DNK
GHA
SWE
CYP
URY
NOR
MEX
FIN
CZE
TUN
AUS
GTM
COL
TUR
ZARMNG
ESP
TGO
COM
CUBISL URY
VEN
IDN
KIR
VNM
SVN
CAN
PER
ESP
STP
FRA ITA
PRT
CZE
MRT
MAR
SVK
ARG
DJIIRQ
COG
MDA
BRB
CHL
LVALTU
VUTGMB
SRB
POL
NOR
PRT
ZMBJAM
ROM
DNK BELGBR
GER
AFG
THA
GEO
LUX
MEX
HND EST HRV
NLD
IRL
PAN
SGP
MYS
ATG BMUBLZ
CAN
KIR
STP
4
Log of GDP relative to the US
ATG
BMU
PLW
3
0
AFG
VUT
5
−2
10
10
4
KIR
DJICAF
ATG
GNQBMU CMR
HTI
LAO
BRA
IRQ
ZAR IRN
PER
ARM
RWA
GMB
SYR
TKM
DOM
AUS
MRT KGZ
VEN
CPV BDI COG
NER
ZAF
SLV NPL
TKM
BEN
MNG
MWI
SYC SUR
BFA
GEO UZB
TGO
BFA
JPN
UZB
TTO
MOZ
MLI
COLNZL
KHM
FJI
FJI
AGO
ERI
AZE ZWE
BHR
AGO
CIV
AZE CHL
ZWE KWT
BLZ
MDG
RUS
ISR ARG
BDI
GTM
BIH YEMBLR
MDA
YEM
ARM
ECU
BRB
SDN
HTIUGA
KEN
SWZ
RWA
ALB
BOL
KAZ
SWZ
LBY ROM TUR
SAU
ZMB
SRB
USA
UGA
MOZ
QAT
CRI
CMR
SEN
PHL
CAF
SDNKAZ
BGD
KGZ
CHN
LKA
SUR
ETH TZA
BHR
SYR
CIV
MDG
ETH
EGY
CUBUKR
IND GERCHN
BLR
TZA
MUS
NPL
BEN
GHA
SEN
NIC
EST
HUN
NIC
JPN
DZA
BIH
MAR
WSMMLI
PAKFIN ITA
LVA
IND
CUB
KEN
MKD
CPV
KWT
DOM
MLT
JOR
LTU
OMN
HND
MUS
BGR
JAM
SLE ALB
SYC
VNM
TTO
UKR POL
PRY
PAN
NGA
GNB
NER
LBY
LKA
URY
BOL
BGD
SLV
JOR
SWE
ITA USA
LBN
PRY
IRN
KOR
GRC
NGA
ISL URY
FRA
SVN
IDN
NLD
THA
MEX
BGR
PAK
IDN
CRI
KHMISL
BRBZAR LAO
GHA
LTU
CZE
SVK
GBR
LUX
BRA
ESP
SWE
CYP
CHE
BEL
TUN
GTM
ZAF
JAM
HRV
AUT
POL
RUS
TUR
PHL
MYS
HUN
TUN
COM
ROM
OMN
PRT
ISR
PER
ECU
ARE
ESP
QAT LVA
DNK
MAR
TGO COG
FIN
EGY
HND
ARE
SRB
NOR
ARG
COL
HRV
KIR
IRL
DZA
MKD
MDA
SVN
NZL
PRT
MEX KOR
ZMBLBN
CAN
NOR
SGP
MRT GEO
VEN
CYPMLT
CHL
AUT
AUS
CZE
SAU
VUTGMB
GBR GER
SVK
IRQ
VNMGRC
THA
STP
EST
DNK
MNGPAN
DJI ATG
BMUBLZ
IRL
MYS
SGP
LUX
AFG
NLD
CHE
BELCANFRA
2
MWI
TCD
0
10
−6
10
Log of GDP relative to the US
−4
−2
10
10
0
10
Log of GDP relative to the US
(c) Extensive margin of exports
(d) Extensive margin of imports
Intensive Theil Index
Intensive Theil Index
Data versus simulation
Data versus simulation
10
8
Red − Simulated Data
Blue − Empirical Data
9
AFG
ERI TCD
GNQ
COM
WSM SLE
1
0
VUT
GNB
STP
3
Red − Simulated Data
Blue − Empirical Data
8
SEN
7
NGA
6
6
ATG
UZB
Import Concentration
Export Concentration
7
SAU
ARECOL
DOM
GEO GHA CIV
ZAF
AZE
VEN
QATCMR
HND
SDN OMN
MEX BRA
ETH ARM MUS SLV
ITA
ECU
IRN ISR
MRT
TZA CUB
IND
ZWE
MKD
PAN
ZAR JAM
CRI HRV NZL
AUS
MLT
NERCOG
THA
ARG
TUR
TTO KAZ
ROM
GNB
MLI
NICKEN
UKR
CHN
TGO
CYP
LBY
SYC
PER
SWE
VNM
SLE FJI
TUN SVK
SRB
LTU
BIH MLI
ZMB
ERI
ZMB SLE
MAR
SUR
ALB
SYR
RUS
MOZ
JAM
EST
BHR
NER
PER
TCD
AGO PAK
POLESP
SGP
MDGNPL
CHL
KIR
TTO
DZA ZAR
HUN
SYC
CPV
MOZ
PHL
GRC
GBR
IDN
WSM MWI GNQ
ZAF
ZWE
KGZ
LVA CMR ECU
IRL
PHL
MYS
GTM
MRTMLT
KGZ
ARE
BLR
ISR
ARG
CHL
AUT
CAF
ISL
GHA
MNG
NOR
CAN
KWT
CRI
LBN
STP
JOR
IRN
SWZ
JPN
UZB
KHM
BMU
SWZ
ARM
EGY
BHR
NPL
AUS
WSM
COG
MUS
JOR
QAT KAZ SAU
BGR
MEX
RUS
BOL
TKM
FINKOR
GER
IND
PAN
HND
TZA
PRY
SEN
MNG
CIV
GMBMDA
CZE
GNQ
BGD
VNM
NZL
BMU
FIN
BRA
LUX
PAK
CUB
HUN
FRA
MDG
THA
BEL
LBN
UKR
LVA
LAO
URY
JPN
KHM
GEO
MYS
PLW
SDN
AGO
TGOMDA
ESP
NIC
BRBUGA
UGA
ATGBLZ
KEN
SWE
SVK
PRY
BLR
PRT
MARNGA
DOM
TUN
KOR
NOR
BDI
LBY
PRT
BOL
GTM
LKA
VEN
CYP
COL
CHE
FJI
GBR
BIHSLV
TUR
IDN NLD
IRL
RWA
MKD
DNK
EST
FRA
ETH
BFA
HTI
SVN
DJI
SYR
YEM
SGP
CZE
BEL
USA
ALB
LTU
GRC
GERCHN
AZE
OMN
BGR
VUT
POL
SVN
BGD
EGY
LKA
YEM
ROM
URY
NLD
VUT BENBLZ
BRB
TKM
BFACOM
SRBDZA
SUR
AUT
HRV
CAN
BENAFG
COM
IRQ
KIR
LAOERIKWT
IRQ
MWI
DNK
TCD
ITA
CPV
CHE
ISL
PLW
DJI
RWA
HTIGNB
LUX
AFG STP
GMB
CAF
5
BDI
4
3
2
1
5
PAN
4
3
BMU
PHL
MLT
SVN MYS
IND
CYPTUN
SGP
ATG
AUT
ISR
OMN IRL
CHE
USA
MLTAFG
FRA
LUX
HRV
DZA
KHM
SAU
BIH
MNG
ARE
CHE
PRY
GRC
QAT
THA
CIV
BHR
MKD
LBY
DJI GNB
KWT
QAT
LBN
BEL
STP
BEL
HUN
ITA
AFG
VNM
ARE
OMN
KOR
NLD
NZL
VUT
GBR
LUX
BGD
CYP
AUS
KGZ
SYC
DZA
SEN
BEN
NOR
EGY
SVK
TTO
ARM
DNK
MEX
IRQ
LAO
NLD
BRA
GRC
ECU
ALB
PAK
CZE
VEN
CHNCHN
IRQ GMB TGO
KEN
ZAF
KHM
PAK
KAZ
YEM
GEO
JOR
RUS
LBN
SLE
ROM
AGO
SDN
CAN
DNK
BGR
GER
IRN
KOR
ISL
NGA
YEM
COL
FIN
GBR
SVK
NER
JPN
MRT
BHR
KIR
SRB
SLV
AUT
CMR
PRT
HND
BOL
GHA
NPL
THA
TKM
GER
ETH
KWT
TZA
IND
LKA
EST
TUN
CHL
CAF
PRY
AZE
SWE
ISR
HUN
JAM
PHL
EGY
JOR
SGP
IRL
CRI
SAU
MDG
GTM
DOM
ERI
MDA
ZMB
COM VUT
GHA
IDN
TUR
BFA
VNM
COG
UZB
BGD
ARG
MLI
TGO
ESP
CPV
BLZ
ZMB
MYS
KAZ
PER
ZAR
SYR
CZE
WSM
ETH
USAJPN
NIC
MRT
ATG
UGA
UKR
MAR
FIN
GEO
MAR
SYC
SDN
LKA
BEN
ESP
MUS
NER
URY
NGA
MLI
CIV
PAN
LBY
RUS
MUS
CHL
FJI
LTU
TUR
AUS
SRB
NOR
HRV
SEN
BOL
UZB
SWZ
SWE
ZWE
SYR
TZA
ALB
MOZ
KEN
LVA
GNQ
CMR
HTI
ARG
URY
ZAF
TCD
PRT
IDN
AGO
GTM
GMB
MDG
ISL
MKD
NZL
SVN
POL
JAM
POLBRA
BDI
ARM
EST
ROM
LTU
RWA
MWI
BGR
COG
LVA
BMU BFA
TKM
PER
UKR
GNQ
BRB
IRN
FRACAN
HND
BDI
SWZ
BLR
AZE
KGZ
MWI
MOZ
SUR
CPV
ZWE
NPL
BRB
CUB
VEN
HTIUGA
COL
BLZ
MNG
ECU
CAF
CRI
ITA
BIH
RWA
TTO
NIC
BLR
MDA
CUB
MEX
DOM
SUR
SLV
PLW
COM
WSM
DJI FJI LAO
ZAR
STP
SLE
ERI TCD
KIR GNB
2
1
PLW
0
−6
10
−4
10
−2
10
0
0
10
−6
10
Log of GDP relative to the US
−4
10
−2
10
0
10
Log of GDP relative to the US
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 4: Simulated (in red) and empirical observed (in blue) export and import concentration versus GDP
across 160 countries. The simulation is based on estimated trade costs form bilateral trade shares including an
exported fixed effect.
29
Number of exporters per product
Number of importers per product
Data versus simulation
Data versus simulation
0.1
0.018
Red − Simulated Data
Blue − Empirical Data
0.09
0.08
0.014
0.012
Frequency
Frequency
0.07
0.06
0.05
0.04
0.01
0.008
0.006
0.03
0.02
0.004
0.01
0.002
0
0
Red − Simulated Data
Blue − Empirical Data
0.016
10
20
30
40
0
0
50
Number of exporters per product
20
40
60
80
100
120
140
160
Number of importers per product
(a) Share of products per exporting country
(b) Share of products per importing country
Figure 5: The simulated (in red) and empirical observed (in blue) share of the number of products traded
against the number of trading countries.
30
Import expenditure versus product share
1
Import expenditure share (1−D)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
AFG ATG
LUX
VUT DJI
HND
SGP
PAN
BMU
ZMB
ZAR
EST
COG
JAM
MKD
MLT
MRT
SVK
GEO
IRQ TGO
QAT GHA
SYC
BIHALB
STP
CHE
MDA
COM GMB
NER
KIR
BLZ
MYS IRL
OMN NLDCYP
LVA
BRB
BEL
AGO
WSM
ARE
NIC
LAO SUR
CAF
NGA TUN
HTI CPV FJI
MDG
KHM
MNG
MEX
MLI
MUS
ISL
LKA
TZA LTU LBN
ARMBEN
THA PHL
LBY ETH
HUNHRV
KGZ MOZ
CRI
KWT JOR
PRY SVN AUT
ISR
UGA
GNB
VNM
NPL
DNKDZA
KAZ
UZB ZWE SEN
CZE
SWZ BHR
SAU
GTM
YEM
BOL
GNQ
BGR
SDN
SLE
BDI
MAR CAN
ERI
SLV
KEN
NOR
BLR
MWI
CHL
GRC
SWE
AZE
CIV
RWA
DOMBGD
SRBPRT
ROM
ECU
TKM
TTO
URYIDN
GBR
BFA
FIN NZL
UKR
EGY
POL
GER PAK
COL
ESP
FRA TUR
PER VEN
TCD
CMRCUB
ZAF
RUSARG AUS
ITA
IRN
KOR
SYR IND USA
CHN
BRA
JPN
0.1
0
0
PLW
0.2
0.4
0.6
0.8
1
Import product share (1−π)
Figure 6: The import expenditure share versus the import product share.
31
Log of average per product import expenditure
Average per product Import Expenditure
−4
10
USA
GER
CHN
GBR JPN
FRA
MEX
IND
ITA
NLDCAN
RUS
ESP
IDN
THA
AFG
KOR
BEL
BRA
CHE TUR
MYS
POL
AUT
IRN
AUS
SAU
PAK
NGA
SWE
PHL
SGP
UKR
VNM
ARE
CZE
KAZ
EGY
DNK
GRC
LBY
HUN
ARG
IRL
ISR
ZAF
NOR
BGD
UZB
COL
PRT
DZA
ROM
SVK
QAT
KWT
ZAR AGO
BLR CHL
SDN
FIN
NPL
HTI
VEN
YEM
CUB
LKA
PER
GEO
TKM
AZE
GNQ
OMN
BGR
LAO
MAR
DOM
ETH
GTM
BIHTZA
TUN
TCD
NZL
ARM
LTU
KHM
SRB
HRV
ECU
LBN
LUX
JAM
GHA
SVN
CRI
HND
ERI
KEN
LVA
UGA
KGZ
PAN
ZMB
VUT
EST
ALBCIV
MLI
SLVSYR
MOZ
NER
BEN
BOL
SLECOG
RWA
PRY
MDA
MKD
BHR
JOR
CMR
MDG
SEN
URY
BMU
BFA
MRTMNG
CYP
MWI
NIC
TTO
DJI CAFTGO
GNB
SURBRB
MLT
MUS
WSM
ISL
FJI
BDI
KIR COMATG
GMB
SYC
BLZSWZ
CPV ZWE
STP
−5
IRQ
10
−6
10
−7
10
−8
10
PLW
−9
10
−5
10
−4
10
−3
−2
10
10
−1
10
0
10
Log of Absorption relative to the US
Figure 7: The log of average per product import expenditure against log of Absorption.
32
Ratio
of import expenditure vs tradable expenditure
2
Log of import expenditure ratio
10
KIR VUT
AFG
STP
GNB
COM
ERI
WSMGNQ
DJI
ATG
BMU
SLE
CAF
ZAR
IRQ
TCDHTI
LAO
GMB
MRT
COG ARM
NER
CPV
SYC
TGO KGZ
GEO
SUR
BEN MNG
BDI RWA
FJI
KHM
NPL
TKM BLZ
AGO
MOZMLI
BIH
UZB
MDA
ALB
MWI ZMB
BRB
QAT
EST
ZWE
BFA
LBY
GHA YEMHND
AZEMKD
MLT
PAN
JAM
UGASENNIC SWZ
LVA
ETHMDG
TZA
PLW
OMN
LUX
KAZ
SDN
BHR NLD
SVK
LTU
MUS
KWT
NGA
CHE
LKA
SGP
BEL
MYS
BLR
LBN CYP IRL
CIV
ISL
PRY
ARE
TUN
THA
JOR
CRI
KENVNM
BGR
SVN
PHL
BOL SLV
HUN
MEX
DOM
CZE
AUT
ISRGER
DNK
GTM HRVTTO
BGDCMR
MAR
SWE
UKR URY
CUB
PAK
IDNDZA
CAN
SAU GBR
ROM
NOR
ECU
SRB
CHL
GRC
ITA
POLPRT
FIN
FRA
EGY TUR
NZL
CHN
SYR
ESP
COL
PER
IRNRUS KOR USA
ZAF
IND
VEN
ARG
AUS
1
10
0
10
BRA
JPN
−1
10
−3
10
−2
10
−1
10
0
10
1
10
Log of GDP per capita relative to the US
Figure 8: The log of the ratio of average per product import expenditure with respect to average per product
tradable expenditure against log of GDP per capita.
33
9
Tables
Table 7: Country-Specific Technology and Trade Costs estimates
Country
USA
AFG
AGO
ALB
ARE
ARG
ARM
ATG
AUS
AUT
AZE
BDI
BEL
BEN
BFA
BGD
BGR
BHR
BIH
BLR
BLZ
BMU
BOL
BRA
BRB
CAF
CAN
CHE
CHL
CHN
CIV
CMR
COG
COL
COM
CPV
CRI
CUB
CYP
CZE
DJI
DNK
DOM
DZA
ECU
EGY
ERI
ESP
EST
ETH
FIN
FJI
FRA
GBR
GEO
Exporter FE
Standard error
Precent cost
Si
Standard error
(lUS /li )q
6,36
-0,46
-1,96
-3,31
2,98
2,19
-3,14
1,12
3,29
2,03
-3,41
-3,45
5,53
-3,11
-4,45
0,96
0,05
-0,83
-3,57
-1,40
-0,26
-1,26
-1,84
3,17
-1,49
-2,05
4,10
4,79
2,13
5,11
-0,12
-2,10
0,87
-0,04
-3,06
-3,16
0,32
-1,47
0,61
1,13
-1,23
2,57
-1,12
-2,29
-0,18
0,42
-4,87
3,76
1,75
-1,73
1,77
-1,88
4,56
4,86
-0,54
0,18
0,25
0,23
0,23
0,19
0,19
0,22
0,45
0,19
0,19
0,22
0,24
0,18
0,23
0,23
0,2
0,19
0,32
0,24
0,21
0,26
0,41
0,22
0,19
0,23
0,26
0,18
0,19
0,2
0,18
0,2
0,2
0,23
0,19
0,29
0,32
0,21
0,2
0,19
0,19
0,28
0,18
0,2
0,2
0,2
0,19
0,26
0,18
0,21
0,2
0,19
0,25
0,18
0,18
0,21
-53,47
5,53
26,79
48,53
-30,12
-23,01
45,55
-12,28
-32,75
-21,83
50,25
51,47
-48,63
45,41
70,73
-10,19
-0,59
10,52
53,34
18,12
3,53
16,48
24,88
-31,64
20,33
28,12
-38,98
-43,90
-22,51
-45,86
1,13
28,96
-9,83
0,63
44,10
46,15
-3,62
19,41
-6,89
-12,91
16,32
-26,76
14,60
31,68
2,25
-4,75
79,16
-36,45
-19,23
23,10
-19,42
25,42
-42,30
-44,32
6,46
0,84
-3,06
-0,97
-0,12
-0,71
1,54
0,2
-3,72
0,98
1,24
1,12
-0,45
-0,89
-0,38
0,6
0,27
1,01
0,26
1,1
2,1
-1,77
-1,91
0,39
1,71
-0,91
-1,11
0,43
-0,76
0,48
1,57
0,06
0,78
-2,63
1,13
-1,78
-0,66
0,06
0,86
-0,44
1,37
-2,99
0,97
0,65
0,61
0,57
0,83
0,12
0,81
-1,36
-0,6
1,73
-0,36
1,05
0,57
-1,25
0,13
0,19
0,16
0,16
0,14
0,14
0,16
0,3
0,13
0,13
0,16
0,16
0,13
0,15
0,15
0,14
0,14
0,23
0,17
0,15
0,18
0,28
0,15
0,13
0,16
0,19
0,13
0,13
0,14
0,13
0,14
0,14
0,17
0,13
0,19
0,2
0,15
0,14
0,14
0,13
0,2
0,13
0,14
0,13
0,14
0,13
0,19
0,13
0,14
0,13
0,13
0,17
0,13
0,13
0,15
1
193,42
23,5
9,63
2,6
2,24
9,61
12,93
1,2
0,77
12,2
49,84
0,85
45,53
36,85
10,69
2,79
1,87
6,66
2,17
8,17
5,66
10,01
2,22
5,95
21,31
0,99
0,9
2,34
2,22
8,73
8,12
16,64
5,89
42,22
16,29
3,43
9,57
3,51
1,04
50,23
0,8
3,72
17,61
6,51
9,14
43,83
1,19
2,27
70,73
0,59
5,57
0,8
1,06
15,46
34
Table 8: Country-Specific Technology and Trade Costs estimates - cont.
Country
GER
GHA
GMB
GNB
GNQ
GRC
GTM
HND
HRV
HTI
HUN
IDN
IND
IRL
IRN
IRQ
ISL
ISR
ITA
JAM
JOR
JPN
KAZ
KEN
KGZ
KHM
KIR
KOR
KWT
LAO
LBN
LBY
LKA
LTU
LUX
LVA
MAR
MDA
MDG
MEX
MKD
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NER
NGA
NIC
NLD
NOR
NPL
NZL
OMN
Exporter FE
Standard error
Precent cost
Si
Standard error
(lUS /li )q
4,74
1,14
-1,69
-3,13
-3,99
0,73
-1,41
1,26
-0,60
-3,14
0,43
4,30
3,76
3,90
-1,18
-3,12
0,08
1,26
3,96
0,76
-0,60
4,91
-0,28
-0,24
-3,04
-2,22
-2,77
4,42
-1,70
-3,15
-0,31
-1,81
0,98
-0,24
1,44
-0,64
0,73
-1,11
-0,95
3,42
-1,04
-2,42
0,30
-2,60
-1,13
-0,58
0,95
-3,87
5,40
-1,89
0,15
-1,13
5,66
1,83
-3,03
2,54
0,39
0,18
0,2
0,24
0,38
0,28
0,19
0,21
0,24
0,19
0,32
0,19
0,19
0,18
0,18
0,2
0,3
0,21
0,19
0,18
0,21
0,2
0,18
0,21
0,2
0,24
0,29
0,39
0,18
0,2
0,29
0,19
0,24
0,2
0,2
0,25
0,21
0,19
0,23
0,22
0,19
0,23
0,25
0,22
0,27
0,21
0,24
0,2
0,23
0,19
0,23
0,21
0,22
0,18
0,19
0,23
0,19
0,21
-43,54
-12,49
23,14
45,90
61,56
-8,59
18,61
-13,99
7,28
45,77
-5,15
-40,26
-36,12
-37,55
15,38
44,88
-1,09
-14,17
-38,00
-8,32
7,39
-44,65
3,08
3,22
43,58
30,65
38,98
-41,23
23,09
46,04
3,77
24,19
-11,03
2,69
-16,07
7,78
-8,11
14,13
12,10
-33,56
13,26
33,82
-3,45
36,44
14,84
7,23
-10,48
59,69
-47,75
25,67
-1,37
14,67
-49,44
-19,87
44,04
-26,41
-4,70
1,17
-1,78
-1,99
-0,89
0,39
0,93
0,41
-2,49
0,92
-0,5
1,49
0,21
1,03
-0,47
1,94
-1,13
-0,18
1,11
1,27
-1,7
0,24
1,95
1,08
-0,06
0,39
0,71
-1,68
1,4
0,84
0,54
-0,23
0,27
-0,37
0,6
-0,65
0,3
0,39
-0,33
-0,93
-0,1
-0,73
-0,45
-0,68
-0,51
-0,55
-2,13
-0,98
0,29
-0,74
-1,35
-1,19
-0,78
-0,88
1,02
0,37
0,58
-0,59
0,13
0,14
0,17
0,27
0,19
0,13
0,14
0,17
0,13
0,23
0,13
0,13
0,13
0,13
0,15
0,21
0,15
0,14
0,13
0,15
0,14
0,13
0,15
0,14
0,16
0,21
0,29
0,13
0,14
0,23
0,14
0,17
0,14
0,14
0,2
0,15
0,14
0,16
0,15
0,13
0,15
0,17
0,16
0,19
0,14
0,17
0,14
0,15
0,14
0,16
0,14
0,15
0,13
0,13
0,16
0,14
0,15
0,65
17,47
30,89
34,18
3,92
2,5
6,43
9,19
2,53
26,46
1,26
4,69
6,78
0,78
7,13
224,32
1,15
1,26
0,8
6,45
5,19
0,48
4,17
20,53
10,95
10,91
20,97
0,73
3,69
11,92
7,97
8,88
7,75
2,6
0,86
2,97
5,1
8,17
20,18
3,27
5,07
43,51
1,64
10,12
18,96
21,41
3,68
34,15
1,64
39,78
57,57
10,6
1,02
0,9
18,27
1,27
6,51
35
Table 9: Country-Specific Technology and Trade Costs estimates - cont.
Country
PAK
PAN
PER
PHL
PLW
POL
PRT
PRY
QAT
ROM
RUS
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SRB
STP
SUR
SVK
SVN
SWE
SWZ
SYC
SYR
TCD
TGO
THA
TKM
TTO
TUN
TUR
TZA
UGA
UKR
URY
UZB
VEN
VNM
VUT
WSM
YEM
ZAF
ZAR
ZMB
ZWE
Exporter FE
Standard error
Precent cost
Si
Standard error
(lUS /li )q
1,59
2,82
0,47
2,33
-9,10
0,87
1,76
-1,36
0,60
0,18
1,98
-3,73
1,34
-2,46
-0,69
6,66
-0,49
-1,74
-1,84
-2,21
-1,59
1,67
-0,38
2,74
-0,81
-1,17
-3,28
-5,68
-1,07
5,42
-4,02
-1,00
0,44
1,92
-0,25
-1,79
0,91
0,76
-2,14
-0,46
2,46
-0,93
-2,40
-2,67
3,49
1,02
1,85
-1,06
0,19
0,23
0,2
0,2
0,4
0,19
0,19
0,22
0,21
0,19
0,19
0,23
0,19
0,2
0,21
0,19
0,49
0,21
0,21
0,33
0,26
0,2
0,19
0,18
0,24
0,28
0,21
0,26
0,23
0,18
0,26
0,22
0,19
0,18
0,2
0,21
0,19
0,21
0,24
0,2
0,19
0,34
0,3
0,22
0,19
0,27
0,26
0,22
-17,08
-28,76
-5,45
-24,40
197,33
-9,93
-19,23
17,79
-7,14
-2,06
-21,33
57,16
-14,98
34,35
8,70
-55,17
5,65
23,31
24,59
29,84
21,12
-18,39
4,59
-28,26
10,26
15,16
48,21
98,20
14,05
-47,82
61,53
12,96
-4,70
-20,57
3,46
24,15
-10,55
-8,63
29,20
5,82
-25,57
11,75
32,82
37,58
-34,26
-11,18
-19,62
13,97
0,9
-2,16
1,17
0,07
4,52
1,78
0,7
0,6
-0,62
1,73
1,89
-0,15
0,36
-0,12
-0,57
-2,19
-0,94
0,42
1,36
-1,89
-0,75
-0,18
1,09
1,41
0,15
-1,46
2,26
0,93
-1,56
-0,68
1,08
0,46
0,01
1,38
-0,88
-0,32
1,75
0,44
0,68
1,35
0,24
-2,46
-1,26
0,39
0,48
-2,97
-2,55
0,16
0,14
0,17
0,14
0,14
0,32
0,13
0,13
0,17
0,15
0,13
0,13
0,15
0,13
0,13
0,14
0,13
0,33
0,15
0,15
0,24
0,18
0,14
0,13
0,13
0,19
0,2
0,15
0,18
0,15
0,13
0,19
0,15
0,14
0,13
0,13
0,14
0,14
0,15
0,18
0,14
0,13
0,23
0,22
0,15
0,13
0,2
0,17
0,16
8,28
6,67
3,64
4,89
0,19
1,57
1,47
7,95
2,69
2,39
2,15
67,74
4,12
41,79
14,97
0,98
22,08
4,95
3,15
30,19
3,21
1,73
1,01
0,66
3,87
3,51
7
52,38
32,81
2,93
12,48
2,04
3,76
2,55
30,26
37,54
3,01
2,72
12,54
4,18
6,16
16,57
9,02
31,04
2,25
58,54
18,9
9,86
36
References
A LVAREZ , F. AND R. J. L UCAS (2007): “General equilibrium analysis of the Eaton Kortum model
of international trade,” Journal of Monetary Economics, 54(6), 1726–1768.
A NDERSON , J. E. AND E. VAN W INCOOP (2003): “Gravity with gravitas: a solution to the border
puzzle,” American Economic Review, 93, 170–192.
——— (2004): “Trade costs,” Journal of Economic Literature, 42, 691–751.
B ALASSA , B. (1963): “An empirical demonstration of classical comparative cost theory,” The Review of Economics and Statistics, 45, 231–238.
B ERNARD , A. B., J. E ATON , J. B. J ENSON , AND S. K ORTUM (2003): “Plants and productivity in
international trade,” American Economic Review, 93, 1268–1290.
B RODA , C. AND D. W EINSTEIN (2006): “Globalization and the Gains from Variety,” Quaterly Journal of Economics, 121, 541–585.
C ADOT, O., C. C ARRÈÈRE , AND V. S TRAUSS -K AHN (2011): “Export Diversification: What’s behind the Hump?” The Review of Economics and Statistics, 93, 590–605.
C HOR , D. (2010): “Unpacking sources of comparative advantage: A quantitative approach,” Journal of International Economics, 82, 152–167.
C OSTINOT, A., D. D ONALDSON , AND I. K OMUNJER (2012): “What goods do countries trade? A
quantitative exploration of Ricardo’s ideas,” Review of Economic Studies, 79, 581–608.
E ATON , J. AND S. K ORTUM (2002): “Technology, Geography, and Trade,” Econometrica, 70(5),
1741–1779.
E THIER , W. J. (1982): “National and international returns to scale in the modern theory of international trade,” The American Economic Review, 389–405.
F EENSTRA , R. C., R. E. L IPSEY, AND H. P. B OWEN (1997): “World Trade Flows, 1970–1992, with
Production and Tariff Data,” National Bureau of Economic Research Working Paper, 5910.
G AULIER , G. AND S. Z IGNAGO (2009): “BACI: International trade database at the product-level,”
.
G OLUB , S. S. AND C. T. H SIEH (2000): “Classical Ricardian theory of comparative advantage
revisited,” Review of International Economics, 8, 221–234.
G RUBEL , H. G. AND P. J. L LOYD (1975): Intra-industry trade: The theory and measurement of international trade in differentiated products, vol. 12, Macmillan London.
37
H AVEMAN , J. AND D. H UMMELS (2004): “Alternative hypotheses and the volume of trade: the
gravity equation and the extent of specialization,” Canadian Journal of Economics/Revue canadienne d’économique, 37, 199–218.
H UMMELS , D. AND P. K LENOW (2005): “The Variety and Quality of a Nations Exports,” American
Economic Review, 95, 704–23.
K OREN , M. AND S. T ENREYRO (2007): “Volatility and development,” The Quarterly Journal of Economics, 122, 243–287.
K RUGMAN , P. (1980): “Scale Economies, Product Differentiation, and the Pattern of Trade,” The
American Economic Review, 16, 670–675.
L EVCHENKO , A. A. AND J. Z HANG (2011): “The evolution of comparative advantage: Measurement and welfare implications,” Tech. rep., National Bureau of Economic Research.
M AC D OUGALL , G. D. A. (1951): “British and American exports: A study suggested by the theory
of comparative costs. Part I,” The Economic Journal, 61, 697–724.
O TTAVIANO , G. I. P., T. TABUCHI , AND J. F. T HISSE (2002): “Agglomeration and Trade Revisited,”
International Economic Review, 43(2), 409–436.
R OMER , P. (1994): “New goods, old theory, and the welfare costs of trade restrictions,” Journal of
Development Economics, 43, 5–38.
S HIKHER , S. (2011): “Capital, technology, and specialization in the neoclassical model,” Journal of
International Economics, 83, 229–242.
S ILVA , J. AND S. T ENREYRO (2006): “The log of gravity,” The Review of Economics and Statistics, 88,
641–658.
S IMONOVSKA , I. AND M. WAUGH (2011): “The Elasticity of Trade: Estimates and Evidence,” Tech.
rep., National Bureau of Economic Research.
WAUGH , M. E. (2010): “International trade and income differences,” American Economic Review,
100, 2093–2124.
38
Appendix Chapter 1
The import expenditure distribution
Note, the set of imported goods is defined as the sum over all the fraction of goods imported from
all other countries in the world economy. Dn,k describes the fraction of goods country n imports
from country k. Given that a country imports a good from only 1 source country (so the sets of
products imported from different countries are mutually disjoint), implies that we can sum the
fraction of goods imported over all trading partners to obtain the probability to import.
I
 Dnk
Pr(imp) =
k6=n
The corresponding distribution function for prices ( p) is given by:
Mn ( p ) =
Mn ( p ) =
b 1 b
w p
s ms
Define u = ÂsN=1 ls ( kns
wns )
1
q
ÂkI 6=n
ÂkI 6=n
´p
0
’sN6=n (1
Gns (q))dGnk (q)
ÂkI 6=n
´p
Dnk
1
Dnk 0 jn 1q q q 1
e
1
jn q q
ÂkI 6=n Dnk
1
1
q q and du = jn 1q q q
p
ˆ
Mn ( p ) =
✓
0
1 dq
u
(e
=
1u
q q dq,
◆
dq
we get:
)du
Hence, the import price distribution is independent of the source countries:
Mn ( p ) = 1
1
jn p q
e
= Fn ( p)
Using the import price distribution, we can derive the import expenditure distribution by the
following transformation. Note, import expenditures of country n on good x in the case of CES
preferences are given by:
qn ( x ) pn ( x ) =
✓
min pi ( x )
i6=n
◆1
h
h
pmn qn
!
b 1 b
=
w p
min B i mi xiq )
kni wni
i6=n
!1
h
h
pmn qn
(19)
and the probability of importing at price p is given by Mn ( p). Hence, we can write the distribution function of import expenditure at price p as
En ( p) = 1
e
1
(1 h )
jn k n
39
1
( p ) q (1
h)
h
where k n = pmn qn is a constant. The corresponding Fréchet pdf is
1
e( p) =
q (1
h)
jn
✓
1 j q (1 h )
n
with location parameter sn = k n
p
kn
◆
✓ ◆
1
e
p
1
q (1 h )
1
p
j n ( k n ) q (1
h)
1 12
.
q (1 h )
and shape parameter a =
Given that the price distribution is independent of the source country and follows a Fréchet
distribution, we can calculate the corresponding concentration indexes analytically. The Theil
index on the intensive margin for country n can be approximately written in terms of the continuos
probability density function:
TnW
=
"
1
Na
Â
k 2 Ga
✓
Rk
R̄ a
◆
ln
✓
Rk
R̄ a
◆#
⇡
ˆ
•
0
✓
R
R̄ a
◆
ln
✓
R
R̄ a
◆
f ( R)dR
Plugging in the density of the Fréchet distribution with location parameter sn = k n
and shape parameter a =
TnW
1
,
q (1 h )
=
ˆ
0
•
1 j q (1 h )
n
we get:
✓
R
R̄ a
◆
ln
✓
R
R̄ a
◆
a
sn
✓
R
sn
◆
a 1
e
( R/sn )
a
dR
where R̄ a is the mean import expenditure. Solving the integral, we get:
TnW
=
1
G (1
1
a)
✓ˆ
0
•
⇣
ln u
( 1/a)
⌘
u
( 1/a)
e
u
du
◆
✓
ln G(1
1
)
a
◆
(20)
The Theil index on the intensive margin of imports does not depend on the scale parameter
sn and is hence identical across countries. The index is completely determined by the shape parameter a =
1/(q (h
1)) and depends only on the elasticity of substitution h and the degree of
comparative advantage q. The integral in equation 20 cannot be solved analytically. To compute
the exact Theil index implied by the shape parameter a, I approximate the integral numerically
via the Gauss Laguerre procedure.
The share of products exported
The concentration index of exports on the extensive margin is given by the number of products
exported to any destination divided by the total number of products in the world. The Law of
Large Numbers implies that a country’s probability to export a good is equal to the share of products exported. Define the set of products that country n exports as the union of the set of products
exported to each destination j, Uex = [ jI6=n B jn . Note that the set of products exported to desti-
nation j overlaps with the set of products exported to destination k. The total number of products
exported to both countries is the sum of the two sets minus the number of products that are in
12 The
generic form of the Fréchet probability density function is: f ( x ) =
40
a
s
x
s
1 a
e
( x/s)
a
.
both, i.e. B jn [ Bkn = B jn + Bkn
B jn \ Bkn . Generalizing this expression to all possible desti-
nations implies that the share of products country n exports follows the Inclusion and Exclusion
Principle and is given by
Uex =
I 1
 ( Ai )
i =1
+
Â
Â
i,j:1i < j I 1
Ai \ A j +
· · · + ( 1) I
Ai \ A j \ A k
i,j,k:1i < j I 1
2
( A1 \ · · · \ A I
1)
where Ai defines the event that a product is exported to destination i, i.e. Ai contains all
⇥
⇤
products where country i obtains the minimum price in country n, Ai = pn,i ( x )  min j6=n pn,j ( x ) .
If we denote the intersection of all Ai ’s with an index L
A L :=
\
( Ai )
i2 L
we can rewrite the set of products exported in a more compact form
Uex =
I 1
Â(
1) k
i =1
Â
1
( AL )
L⇢{1,...,I 1},| L|=k
The last sum runs over all subsets L of the indices 1, ..., I
1 where k describes the number of
destinations a product is exported to.
In the special case of symmetric countries, the number of products exported to k destinations
is the same for all destinations and the intersection A L only depends on the cardinality of L. As a
result, we can write the event to export to k destinations as the L = k intersections of A,
ak = ( A L )
and the set of products exported simplifies to
Uex =
I 1
Â(
1) k
k =1
where I
1
I
k
!
ak
1 is the total number of destinations. The resulting share of products exported can
readily be calculated. For example, the share of products country n exports to one particular
destination j is given by a1 = D jn and is equal to
a1 =
(k )1/q
(1 + ( I 1)(k )1/q )
The share of products country n exports to any pair of destinations j and k is given by the
41
probability of obtaining the minimum price in those two destinations.
a2 = Pr
⇢✓
◆ ✓
◆
⇥
⇤
p j ( x ) = pij ( x )  min plj ( x ) ^ pk ( x ) = pik ( x )  min [ plk ( x )]
l 6 =i
l 6 =i
⇥
⇤
where the price of good x offered by country n in destination j is pn,j ( x ) = B kxnq 8 j 6= n and
at home pn,n = Bxnq . Notice that the only difference between prices offered is whether the country
sells in the home market or in the foreign market. Since k < 1 implies that p jj < p jl and pkk < pkl
8 j, k 6= l, we can write the set of products exported to destinations j and k by the corresponding
probability to obtain the minimum price in the respective destinations
⇢
h
i
a2 = B Pr kxiq  min x qj , xkq , kxlq
l 6=i,j,k,
By the properties of the exponential distribution, this probability is equal to
a2 =
(k )1/q
2 + ( I 2)(k )1/q
Proceeding in similar manner, one obtains the probability to export (and hence the share of
products exported) to k destinations
ak =
(k )1/q
k + ( I k )(k )1/q
As a result, we can write the extensive Theil for exports as the inverse of the log of the share of
products exported to any destination as
ext
Ti,X
=
ln
I
Â(
1)
i =1
k 1
I
k
!
ak
!
where
ak =
(k )1/q
k + ( I k )(k )1/q
Trade Data
To build my empirical evidence, I use the BACI data based on the Comtrade data set collected by
the United Nations. I choose the 6 digit HS 1992 product classification scheme as the preferred
level of disaggregation. I assume that the tradable goods sector corresponds to manufactures defined to be the aggregate across all 34 BEA manufacturingindustries. Using a correspondance
table provided by Feenstra et al. (1997), I identify 4529 tradable manufacturing products. I construct trade shares D following Bernard et al. (2003) and Waugh (2010) in the following manner:
Di,j =
Importsi,j
Gross Mfg. Productioni - Exportsi + Importsi
42
In the numerator is the aggregate value of manufactured goods that country i imports from
country j. These data are directly obtained from BACI. In the denominator is gross manufacturing
production minus total manufactured exports plus manufactured imports (against all countries
in the sample), see Eaton and Kortum (2002). Basically, this is simply computing an expenditure
share by dividing the value of inputs consumed by country i imported from country j divided by
the total value of inputs in country i. Gross manufacturing data are from either UNIDO (2012) or
imputed from value added data from the UN National accounts.
SITC 4 digit industry classification
In the paper I analyzed total net trade flows at the 6 digit HS industry classification. This sections
shows that the results found in the main part of the paper are not driven by the industry classification scheme and do apply in a more general sense. As a robustness check, I use the 4 digit
SITC and the 6 digit NAICS classification scheme. The total number of SITC products is 642 and
of NAICS products is 460.
Table 10: Mean concentration indexes for net trade flows based on the 4 digit SITC and the 6 digit NAICS
industry classifications: 160 countries
Gini
Mean index
(SITC 4 digit)
% share of overall
concentration
Correlation to HS
Mean index
(NAICS 6 digit)
% share of overall
concentration
Correlation to HS
X
M
0.98
0.86
Theil Exports (X)
Ext. Int.
Total
Mar. Mar.
Theil Imports (M)
Ext.
Int.
Total
Mar. Mar.
2.16
1.86
0.70
1.29
54%
46%
35%
65%
4.01
1.99
0.95
0.88
0.94
0.91
0.95
0.62
0.83
0.73
0.97
0.82
1.98
1.75
3.73
0.52
1.23
1.75
53%
47%
29%
71%
0.95
0.86
0.53
0.79
0.94
0.89
0.91
0.66
Table 10 present the descriptive statistics for the SITC and the NAICS sample. The qualitative
estimates of the SITC as well as the NAICS classification are very similar to the HS one. Exports
are more concentrated than imports. Concentration is driven by the extensive margin for exports
and on the intensive margin for imports. Also, in terms of cross country evidence, larger countries
import and export more goods. Strikingly, the L pattern of the extensive margin also appears
when using the SITC and the NAICS classification.
43
Poisson parameter approach
The data contains intra-industry trade whereas the model is a pure Ricardian model. In this section
I outline an alternative approach that converts the measurement of product units in the model to
product units in the data. Suppose that the true level of disaggregation of Ricardian products,
as defined in the Eaton and Kortum (2002) model, is unobserved and the classification scheme
measures only an aggregate of those Ricardian products. For example, when products, in the
sense of the Eaton and Kortum (2002) model, arrive at the boarder, custom agents aggregate those
products into an industry according to the HS classification standard. The number of EK model
products that custom agents assign to an HS industry classification is unobserved to the researcher.
Given this interpretation, I model the classification process as a randomization device following a
Poisson process with parameter µ. The parameter µ informs on how many EK Ricardian products,
on average, comprise one HS code (the observed product unit in the data).
To estimate the Poisson parameter, I proceed as follows. By the law of large numbers, the
probability of importing a particular EK product equals the share of the number of EK products
imported with respect to the total number of EK products. In the model, the probability that an EK
product is imported equals P(imp EKprod ) = 1
Dii , where Dii is the probability of not importing
an EK product. By independence, the probability of not importing any EK product within an HS
µ
code is Dii , where µ is the average number of products that comprise an HS code. As a result,
we get the probability of importing an HS code (product unit in data), which corresponds to one
minus the probability of not importing any EK products in that industry, P(imp HScode ) = 1
µ
Dii .
Since the probability of importing a product just equals the share of products imported, NM /N,
we can use the definition of Theil index on the extensive margin,
Tibm =
ln( NM /N ) =
ln 1
µ
Dii
to back out µ:
µi =
exp( Tibm )
ln( Dii )
ln 1
!
We compute the Poisson parameter for each country and take the average value as our estimate
of µ̂ = 1/I ÂiI=1 µi . The results imply that on average µ̂ = 0.94 EK products comprise an HS code.
code.
Empirical evidence and simulation results
In my simulation the total number of intermediate goods (N) is the product of the 4529 industries
in the data times 0.94, the average number of products in an industry, N = 4258. One advantage
of the this approach is that we can make use of the full data sample and do not lose 35 percent of
trade flows when converting the data into net trade flows. Next, I present the empirical results for
44
the full sample together with the corresponding simulation results that replicate the data.
Table 11: Mean concentration indexes for gross trade flows over 2880 country-year pairs
Gini
Level of
concentration
% share of total
concentration
Correlation
with net trade
Theil Exports (X)
Theil Imports (M)
X
M
Ext.
Mar.
Int.
Mar.
Total
Ext.
Mar.
Int.
Mar.
Total
0.96
0.89
1.81
2.59
4.40
0.94
1.76
2.70
41%
59%
34%
66%
0.96
0.45
0.98
0.82
0.98
0.87
0.87
0.90
Figure 11 shows that, in general, the pattern of export and import concentration in the full
sample is similar to the pattern in the net trade flow sample. Exports are more concentrated
than imports for almost all countries and on all margins. The pattern on the extensive margin is
displayed in Figure 11(d). Figure 11(c) shows the patterns on the intensive margin.
Turning our attention to the quantitative differences, we observe that the overall level of concentration decreases with respect to the net trade flow sample for both exports and imports. The
decomposition reveals that the effects are different across the margins. In the case of the extensive
margin, concentration decreases with respect to the net trade flow sample whereas on the intensive margin concentration increases thus reversing the relative importance of each margin in terms
of overall export concentration. Intra-industry trade increases the number of products traded and
the sales value of the respective product. As a result, we observe a lower (higher) concentration
index on the extensive (intensive) margins. The overall concentration index is primarily driven by
the intensive margin with a share of 59% for exports and 66% for imports, see Table 12.
Table 12: Simulated concentration level with Poisson parameter µ = 0.94 and exporter fixed effect
Gini
Model
Simulation
Data (gross trade flows)
Data (net trade flows)
X
M
0.99
0.89
0.96
0.89
0.98
0.91
Theil Exports (X)
Ext. Int.
Total
Mar. Mar.
Theil Imports (M)
Ext.
Int.
Total
Mar. Mar.
4.97
60%
1.81
41%
2.60
1.15
39%
0.94
34%
1.10
45
3.32
40%
2.59
59%
2.13
8.29
4.40
4.73
1.76
61%
1.76
66%
1.61
2.91
2.70
2.71
Using data on gross trade flows I re-estimate trade cost and technology parameters based. I
then simulate the model, calculate the resulting concentration indexes using the Poisson measurement device and compare the simulated results with the data. Table 12 shows the results. Export
concentration is higher than import concentration on all margins. With respect to the decomposition, similar to the net trade case, the extensive margin dominates overall concentration for exports
and the intensive margin dominates for imports. The obtained concentation levels of imports are
close to the one in the data. In the case of exports, simulated concentration levels are too high. In
terms of the cross country concentration pattern, the calibrated model fits the data well, particularly for exports. However, similar to the net trade results, the model cannot capture the negative
relationship between import concentration and GDP.
46
8
Mean of the index from 1992−2009
IND
USA
CHN
.85
ITA
GER
6
TLS
NGAYEM
OMN
ATG
KWT
MLI TCD
BWA
SAU
IRN
GAB
COM GNB
NER
BDI
ARE SYC
QAT
KIR
COG
RWA
WSM
GIN
SDN
SURCAF
STP VUT
TKMAZE
DZA
BFA
VEN
BEN MRT
DJI
MWI
BMU
MNE
MOZ
SYR
BLR
JAM
ZMB
GMB
UGA
ETH
CUB
BHS
ECU
NOR
CPV
HKG
PNG
CMR
TTO
GHA
KAZ
GUY
MNG
SLE
ERI
CIV
TGO
PRY
ISL
MLT
BLZ
MUS
BHR
KHM
JOR
ARM
MAC
SWZ
BRB
BOL
FJI
CRI
SEN
NPL PHL
COL
SLV
BGD
LBN
CYP
PAN
SGP
RUS
CHL
MDG
KEN
NIC
HND
GEO
ZWE
KGZ
TZA
IRL
ALB
VNM
PER
GTM
EGY
LUX
PAK
DOM
MDA
BIH
MEX
TUN
LVA
AUS
LKA
GRC
MYS
URY
MKD
ZAF
NZL
ARG
MAR
PRT
FIN
ISR
ESP
SRB
SVK
CAN
LTU
CHE
AFG
EST
HUN
HRV
FRA
KORUKR
GBR
BRA
ROM
IDN
DNK
SVN
TUR
SWE
BGR
AUT
THA
BEL
POL
JPN
CZE
IND
NLD
CHN
GER
ITAUSA
4
.95
.9
NLD
Export Concentration
JPN
CZE
2
1
Total Theil index − SITC 4 digit
0
.75
.8
Export Concentration
Gini index − SITC 4 digit
Mean of the index from 1992−2009
TLS
TCD
NGA
YEM
GAB
OMN
ATG
BWA
GNB
BDI
COM
KWT
QAT
SYC
COG
NER
RWA
MLI
CAF
SAU
DZA
GIN
STPKIR
SUR
MRT
DJI
VUT
TKM
WSMSTP
WSM
BMU
BEN
BFA
SDN
IRN
MWI
MOZ
GMB
ZMB
MNE
JAM
ARE
CUB BHS
CPV
PNG
ETH
CMR
UGA
VEN
GUY
HKG
ECU
AZE
ERI
GHA
TTO
ISL
MNG
BLR
SYR
CIVMAC
PRY
KHM
MUS
TGO
BHR
KAZ
SLE
BLZ
MLT
NORSWZ
FJI
SEN
ARM
BRB
BOLJOR
LBN
SLV
NICBGD
MDG
NPL
CRI HND
CYP
SGP
TZA
COL
KEN GEO
CHL
KGZ
ALB
PER
ZWE
PAK
GTM
DOM
PAN
RUSIRLLUX
VNM
BIH LVATUN
PHL
MDA
GRC
LKA
EGY
NZL
MAR
URY
MEX
MKD
AUS
PRT
MYS
ARGESP SRB
ZAF
EST LTUFIN
AFG
HRV
CAN ISR
SVK
ROM
CHE
KOR UKR
BRA
TUR
SWE
SVN DNK
FRA HUN
GBR
BGR THA
POL
AUT BEL IDN
.75
.8
.85
.9
.95
1
0
2
4
Import Concentration
(a) Gini coefficient
8
(b) Theil index
Mean of the index from 1992−2009
KWT
2
VEN
AZE BLR
NOR
MLI
QAT
ECU
RUS
SDN
TKM
GAB
MOZ
PHL
KAZ
DZA
ATGCOL
NER
JAM
ZMB
CIV
GIN
MNE
SUR
MWI
CHL
PAN
CRI
IRL
TTO
BWA
ZAF
VNM
SGP
COG
HKG
PRY
UGA
CMR
MYS
SYC
BFA
GHA
MLT
KEN
ZWE
SLE
BEN
MEX
EGY
ETH
MRT
GNB
CUB
MNG
MUS
AUS
TCD
ISL
PER
JOR
KHM
CAF
CHE
PAK
ARG
FRA
HND
BOL
SLV
CAN
VUTBMU
BHS
PNG
MAC
LUX
BHR
FIN
BRA
HUN
SVK
ESP
CYP
GUY
KOR
TGO
GTM
IDN
BGD
BDI
NPL
ISR
MDG
GBR
JPN
TLS RWA
NZL
LVA
DJI
THA
GEO
TUN
CHN
ARM
SWZ
WSM
IND UKR
BLZ
GER
DOM
SWE
URY
NIC
PRT
LKA
KGZ
TZA
FJIBIH
SRB
MDA
DNK
AFG
SEN
CZE
LBN
GMB
BEL
MAR
EST
MKD
TUR
NLD
AUT
SVN
POL
BGR
ROM
GRC
ALB
LTU
USA
STPBRB HRV ITA
COM
KIR
CPV
COM
KIR
STP
GNB
VUT
AFG
0
ERI
0
TLS
TCD
ERI
BDI
CPV
RWA WSM
BWA
GMBCAF
ATG
DJI SYC
COG
NER
GAB BFA
MRT BMU
BEN
SUR
GIN
MLI
BHS
MWIBLZ
CUB
MNE
DZA
ETH
BRB
PNG
UGA
SDN
QAT
GUY
TKM
YEM
JAM
ZMBMNG TGO
MOZ
FJI
SEN
ARM
SWZ
HKG
LBN
CMR
OMN
ISL
GHA
BHR
NGA
SLE
AZE
MUS
ALB
KHM
TTO
NIC
MAC
MLT
JOR
NPL
PRY
BGD
BOL
KGZ
TZA
MDG
GEO
KWT
CYP
SLV
CIVMDA
BLR
ECU
VEN
BIH
KAZ
GRC
DOM
HND
SAU
GTM
MKD
LKA
KEN
HRV
TUN
CRI
LTU
MAR
ZWE
SGP
NOR
LUX
URY
LVA
PER
PRT
SYR
P
PAN
AN
PAK
ARE
SRB
CHL
IRN
EST
COL
NZL
EGY ISR
IRL
VNM
ROM
AUS
MEX
SVN
PHL
ESP
FIN
BGR
TUR
ARG
SVK
DNK
POL
AUT
CAN
BEL
RUS
HUN
SWE
MYS
UKR
KOR
GBR
CHE
ZAF
CZE
IDN
FRA
THA
BRA
ITA
USA
JPN
NLD
IND
GER
CHN
4
4
ARE
NGASAU
OMN
YEM
SYR
2
Export Concentration
6
Extensive Theil index − SITC 4 digit
Mean of the index from 1992−2009
6
Intensive Theil index − SITC 4 digit
IRN
0
Export Concentration
6
Import Concentration
2
4
6
0
Import Concentration
2
4
Import Concentration
(c) Intensive margin
(d) Extensive margin
Figure 9: Export versus import concentration on the 4 digit SITC level
47
6
Gini index − NAICS 6 digit
Total Theil index − NAICS 6 digit
Mean of the index from 1992−2009
Mean of the index from 1992−2009
6
AGO IRQ
NGA
BRN
YEM
TCD GNQ
OMN
IRNCOG
QAT
SAU
GAB
GNB
KWT
MLI
AZE TKM
BDI
BFA
VEN
RWA
BEN
STP
MRT
MDV
COM
NOR SUR
SDN
TJK
LCA
ISL
MOZ
MWI
JAM
KNA
CAF
ZMB
GIN
NER
GMB
ATG
VCT
SYR
DJI
CPV
GRD
HKG
COD
ECU
ETH
TTO
BHR
GHA
PRY
UGA
KAZ CMR
SGP
SLE
TGO
MNG
BTN
UZB
RUS DMA
BOL
MUS
SEN
MAC
CIV MLT
JOR
BLZ
LAO
KGZ
KHM
ARM
CRI
COL
BGD
LBN
IRL
FJI
BRB
CHL
PER
CYP
HND
GEO
UKR
EGY
SLV
KEN
TZA
MDG
ZWE
NPL
GTM
BIH
PAK
ALB URY
MDA
AUS
DOM
PHL LTU BLR
TUN
LVA
NZL
MYS
ZAF
LKA
ARG
FIN
MAR
GRC
SRB
CAN
MEX
VNM
ESP SVK
HUN
HRV
KOR
BRA
ISR
PRT
GBR
EST
BEL
FRA
ROU
CHE
SWE
DNK
IDN
BGR
NLD
SVN
AUT
TURCZE THA
POL
JPN IND
USA
Export Concentration
3
4
5
Export Concentration
.8
.9
1
AGO
IRQ
BRN
NGA
TCD
YEM
GNQ
GAB
QAT
OMN
GNB
SAU
COG
MLI
TKM
RWA
BDI
MRT
AZE
COM
BEN
IRN
BFA
MDV
STP
VEN
SUR
ISL KWT
KNA
LCA
TJK
CAF
SDN
MOZ
GIN
JAM
GRD
GMB
VCT
CPV
COD
NER
MWI
DJI
PAN
HKG
ZMB
NOR
ECU
ETH
TTO
BHR
GHA
CMR
TGO
MNG
PRY
UGA
SLE
BTN
DMA
SEN
MAC
MUS
SGP
KAZ
BOL
SYR
UZB
CIV
JOR
LAO
KHM
ATG
ARMBGD
LBN
RUS
BLZ
FJI
MLT
BRB
KGZ
PER
CYP
GEO
HND
COL
IRL
CHL
TZA
CRI
SLV
MDG
KEN
PAK
GTM
BIH ALB EGY
NPL
ZWE
URY
DOM
MDA
TUN
AUS
UKR
LKA
LVA
GRCNZL
PHL
ZAF
ARGMAR
SRB
MYS
BLR
LTU
VNM
CAN ESPFINMEX
SVK
HRV
PRT
KOR
BRA
HUN
ROU
ISR
EST GBR
BEL
SWE CHE
FRA
DNK
BGRIDN
TWN
NLD
SVN
THA
TUR
AUTPOL
IND
JPN
USA
CZE
CHN
2
DEU
ITA
PAN
TWN
1
.7
DEU CHN
ITA
.7
.8
.9
1
1
2
3
4
Import Concentration
Import Concentration
(a) Gini coefficient
Between Theil index − NAICS 6 digit
4
Mean of the index from 1992−2009
4
Mean of the index from 1992−2009
COM
AGO
MDV
IRN
CPV
GNQ
GNB
BRN
MRT
TCD
GRD
KNA
IRQ
DJI
QAT
HKG
GMB
LCA
GAB
RWA
BDISTP
BEN
SDN
KWT
COD
COG
YEM
SUR
VCT
GIN
TKM
JAM
BFA
NGA
MLI
BHR
ISL
PAN
CYP
MOZ
AZE
ATG
LBN
ETH
BRB
NER
MNG
SEN
CAFBTN
MAC
FJI
DMA
TJK
JOR
SAU
MLT
MUS
BLZ
TGO
CMR
VEN
OMN
KHM
UGA
MWI
GHA
ARM
PRY
ALB
ZMB
KAZ
SLE
LAO
GEO
BGD
TTO
BIH
SLV
BOL
UZB
TZA
GRC
ECU
SGP
NPL
DOM
KGZ
HND
MDG
NOR
URY
MDA
CIV
LVA
EGY
GTM
MAR
SRB
SYR
RUS
PER
KEN
HRV
CHL
PAK
IRN
CRI
LKA
TUN
LTU
IRL
PRT
AUS
NZL
EST
ZWE
ISR
COL
ROU
ARG
BLR
VNM
UKR
BEL
ESP
GBR
PHL
SVK
FIN
CAN
ZAF
AUT
BGR
SVN
DNK
POL
CHE
HUN
MEX
NLD
TUR
FRA
BRA
SWE
MYS
KOR
USA
CZE
JPN
IDN
TWN
THA
IND
ITA
DEU
OMN
YEM IRQ
SYR
AZEBRN
MLI
COG
TKM
TCD
BFA
ECUZMB MWI
TJK
KWT
RUS COL
AGO
GABQAT
GNQ
CRIIRL BDI
UKR
SGP
TTO
CAF
ISL MOZ STP
BEN
NER
CHLUZB
PER
GNBCIV
JAM
RWA
KAZMYS
ZWE
ATG
SLE
GHA
BOL
PRY
KGZ
SUR
SDN
UGA
PHL
BLR
KEN
ETH
MEX ZAF
EGYCMR
LAO TGO
GIN
PAK
FIN
CAN
MRT
KOR
VCT
AUS
GTM
DMA
HUN
BGD
BTN
MNG
TWN
HND
NZL
MUS
ARG
BHR
SVK
LCA
TUN
IDN
BRA
BLZ
ARM
MDG
KHM
VNM
JOR
LTU
COD
ESP
MAC
SEN
FRA
MLT
NPL
TZA
KNA
URY
LKA
THA
SWE
GEO
SLV
GMB
IND
MDA
CHE
DNK
LVA
BIH
FJI DJI
GBR
HKG
BEL
CHN
SRB
MAR
JPN
LBN
NLDBGR
DOM
BRB
CZE
GRD
ISR
ALB
MDV
DEU
ROU
EST
TUR
USA
PRT
SVN
HRV
POL COM
CYP
GRC
AUT
CPV
ITA
NOR
VEN
1
Export Concentration
1
2
3
NGA
SAU
2
Import Concentration
PAN
CHN
0
Export Concentration
2
3
6
(b) Theil index
Within Theil index − NAICS 6 digit
1
5
3
4
0
(c) Intensive margin
1
2
Import Concentration
3
(d) Extensive margin
Figure 10: Export versus import concentration on the 6 digit NAICS level
48
4
Gini index − HS 6 digit
Total Theil index − HS 6 digit
Mean of the index from 1992−2009
Mean of the index from 1992−2009
8
6
4
2
Export Concentration
.95
.9
.85
ITA
GER
NGA
TCD
YEM
OMN
GAB
COG
IRN
KWT
GNB
SAU
QAT MLI
COM
ARE
BFA WSM
BWA AZEBDI
RWA
KIR
BMU
BEN
SDN MWI
GIN
STP
CAF
VEN DZA ZMB MRT
NER
VUT
CUB
JAM
ECUSYR
MOZGMB BHS
PNG
ETH
UGA
SLEERIATG
CMR
GUY
CPV
GHA
PRY
NOR TTO
MNG
TGO
ARM
KAZ BHR
DJI
BOL
ISL
MLT
BLZ
BRB
KGZ
TZA
MUS
CRI
COL
ZWE
FJI GEO
SEN
NIC
KHM
RUS
PER
PAN
KEN
SWZ
CHL
MDGNPL
JOR
HND
EGY
SLV
LBN
LVA
GTM
BGD
CYPPHL
ALB
VNM
AUS
DOM
IRL
ARG
LTU MDA
BIH
ISR
MEX URY
AFG
MYSBLR
CAN
NZL
MKD
TUN
MAR
ZAF
GRC
LKA
EST
PAK
BRA
FIN
UKR
SRB
HRV
SVK KOR
PRT
HUN
IDN
ROM
BGR
DNK
SWE
THA
ESP
SVN
IND
TUR
POL
CHE
FRA
GBR
CZE
BEL
JPN
NLD
AUT
USA
CHN
GER
ITA
0
.75
.8
Export Concentration
1
TCD
GAB
KWT
COM
YEM
COG
GNB
QAT
BDI
KIR
RWA
MLI
MRT
WSM
DZA
OMN
GIN
BMU
CAF
BEN
BWANGA
VUT
SDN
SAU ARE
STP
MWI
BFA
PNG
NER
CUB
GMB
ZMB
AZE
CPV
MOZ
BHS
CMR
GUY
IRN
ETH
JAM
ERI
GHA
VEN
ECU
UGA
TTO
PRY
MNG
TGO
BHR
BOL
ISL
DJI
SLE
ATG
BRB
ARMATG
ARM
SEN
KAZ
TZA
BLZ
NIC
MLT
MUS
KGZ
FJI
KHM
SYR
GEO
MDG
ZWE
HND
JOR
PER
NORCOL
CRI
SWZ
CHL
BGD
KEN
SLV
LBN
DOM
PAN
ALB
GTM
CYP
MDA NPL
URY
RUS
LVA
EGY
IRL
BIH
AUS
ARG
NZL
LTU
MARVNM
MKD
PHL
GRC
LKA BLR
AFG
PAK
MYS
MEX TUN
EST
SRB
CANHRV
ISR
ZAF
FIN
PRTROM
BRA UKR
SVK
HUN
BGR
DNK
KOR
IDN THA
POLSVNSWE
TUR
ESPCHE
AUT
CZE
BEL
GBR
IND
NLD
FRA
JPN
USA
CHN
.75
.8
.85
.9
.95
1
0
2
4
Import Concentration
(a) Gini coefficient
8
(b) Theil index
Mean of the index from 1992−2009
6
Extensive Theil index − HS 6 digit
Mean of the index from 1992−2009
6
Intensive Theil index − HS 6 digit
4
0
PAN
KIR
TCDSTP
GNB
WSM ERI
CPV
BDI
VUT
RWA
BMU
BENGMB CAF
DJI
GAB
COG
MRT ATG
BFA
GIN
MWI
SDN
BWA BHS PNG
MLI
MOZNER
ETH
DZA
GUY
BRB
CUB
UGA
QAT
YEM
MNGARM
BLZ TGO
ZMB
KWT CMR
JAM
SEN AZE
NGA
GHA
PRY
NIC
SLE
BOL
KHM
TZA
BHR
FJI
ISL
TTO
OMN
ALB GEOKGZ
MLTMDG
LBN
NPL
KAZ
MUS
CYP
AFG
JOR
SAU
SLV
VEN
ECUHND
DOM SWZ
BIH
MDA
BGD
URY
GTM
ZWE
CRI
MKD
KEN
PAN
GRC
LVA
IRN
PER
HRV
LKA
SRB
CHL
MAR
SYR
LTU
TUN
ARE
BLR
NOR
EGY EST
COL
NZL
VNM
ARG
PRT
ISR
IRL
PHL
ROM
AUS
PAK
UKR
FIN
MEX
SVK
SVN
CAN
BGR
HUN
DNK
POL
RUS
MYS
TUR
AUT
CHE
SWE
ESP
CZE
BRA
BEL
ZAF
GBR
IDN
THA
KOR
NLD
FRA
USA
IND
JPN
ITA
GER
CHN
2
4
2
Export Concentration
COM
IRN
ARE
NGA
OMN
SAU
YEM
SYR
KWT
VEN
NOR
QAT
ECU
MLI
AZE
RUS
GAB
COG
BWA
COL
ZMB
BFA
DZA
JAM
TCD
SLE KAZ
SDN
MWI
CRI
TTO
GIN
ZWE
CHL
PER
CMR
NER
CUB
EGY
GHA
PRY
KEN
AUS
MRT
MOZ
IRLMYS
BEN
VNM
BHR
MLT PHL
ETH
UGA
MEX
PNG
MUS
ARG
ISL
ISRZAF
BDI
GUY
CAF
SWZ
CAN
BOL
MNG
BHS
LVA
BRA
BMU
KGZ
RWA
GNB WSM
TGO
KORIND
JOR
HND
NPLGTM
ARM
TZA
LTU
IDN
GEO
FJISLV
BGD
BLR
GMB
ATG
MDG
NZL
PAK
FIN
THAJPN
HUN
FRA
UKR
BLZ
SVK
KHM
NIC
SEN
TUN
URY
MDA
LBN CYP
SWE
DOM
ESP
MAR
STPBIH
GBR
PRT
VUT
NLD
BRB
EST
CHE
DNK
TUR
CHN
MKD
LKA
BGR
GRC USA
CZE
BEL
ALB
POL
GER
ROM
SVN
SRB
KIR COM DJI
AUT
HRV
AFG
ERI CPV
ITA
0
Export Concentration
6
Import Concentration
0
2
4
6
0
Import Concentration
2
4
Import Concentration
(c) Intensive margin
(d) Extensive margin
Figure 11: Export versus import concentration for gross trade flows
49
6
8
6
VUT BMU
AFG
PAN
ATG
GNB
ERI BHS
COMWSM
ARM
DJI CAF
SLE TCD
IND
NER
MRT
TGO
BEN
BFA
KHM
KGZKHM
KGZ
GMB BDI
MLI GEO
RWA
GIN
MNG
PHL
MWI
MLT
MDA
UKR
NPL
JPN
CPV ZWE
YEM BLR
KOR
COG
FJI
USA
SEN
MOZ
AZE
BHR
UGA
CYP
GUYSWZ PNG
ZMB
MDG
ETH
LTU
ZAF
BLZ
ITAGER CHN
TZA
QAT
THA
PRY
SDNCUB
LBN
JAM
KEN
MYS
BRBNIC
OMN
PAK
GHA
ALB
CMR
BIH
JOR
BGR
BGD
TTO
HND
DOM
ISR
SVK
NLD
GAB
SYR
URY
LKA
HUN
BRA
KAZ
KWT
VNM
IRL
MUS
SRB
MAR
SLV
FIN
TUR
CHE
FRA
EST
DZA
IDNESP
BWA
SWE
EGY
GRC
BELIRN
BOL
CHL
ISLMKD
GTM
CRI
CZE
NGA
LVA
ROM
NZLPER
ARE
ECU
PRT
TUN
SVN
AUT
HRV
GBR
SAU
POL
RUS
AUS
MEX
COL
NOR
DNK
VEN
ARG
CAN
0
JPN
USA
GER CHN
STPKIR
4
6
4
2
ITA
Import Concentration
NGA
TCD
YEM
OMN
GAB
COGMLI
IRN
GNB
QAT KWT ARE SAU
COM
WSM BDI
BFA BWA AZE
RWA
BMU
BEN
SDN DZA
GIN
MWI
MRTNER
CAF
ZMB
VUT
GMB
CUB VEN
JAM
ECU
MOZ
PNG UGA
ETHSYR
BHS
SLE
ATG
CMR
CPV GUY
ERI MNG
GHA
PRY
NOR
TTO
TGO ARM
BHR
KAZ
DJIBLZ
BOL
ISL
MLT
BRB
KGZ
TZA
MUS
CRI
COL
ZWE
FJISWZ
SEN
NIC
GEO
KHM
RUS
PER
PAN
NPL
KEN
CHL EGY
MDG
JOR
HND
SLV
LBNGTM BGD
LVA
CYP
ALB
VNM
PHL ARGAUS
IRL
MDA
LTU DOM
BIHURY
ISR
MEX
BLR
AFG
CAN
NZL MYS
MKD
TUN
MAR
ZAF
GRC
LKA
EST
PAK
BRA
FIN
UKR
SRB
HRV
SVK HUN
PRT
KOR
IDN
ROM
BGR
DNKCHE
SWE
THA
ESP
SVN
IND
TUR
POL
FRA
GBR
CZE
BEL
NLD
AUT
STPKIR
2
8
Total Theil index − HS 6 digit
Mean of the log index from 1992−2009
0
Export Concentration
Total Theil index − HS 6 digit
Mean of the log index from 1992−2009
−10
−5
0
−10
Log of GDP relative to the US
−5
0
Log of GDP relative to the US
R2=0.39
R2=0.38
(a) Overall concentration of exports
(b) Overall concentration of imports
Mean of the log index from 1992−2009
6
Extensive Theil index − HS 6 digit
Mean of the log index from 1992−2009
6
Extensive Theil index − HS 6 digit
4
Import Concentration
USA
0
4
2
KIR
STP
2
TCD
BDI
ERI RWA
BMU
GMB
BEN GAB
DJI CAF
COG
MRT GIN
ATG
BFAZAR
MWI
SDN
BWA
BHS
NER
PNG
MLI
MOZ
ETH
GUY
BRB
UGA
QAT CUBDZA
YEM
MNG
BLZ TGO
ARM
ZMB JAM
LAO
KWT
AZE
CMR
SEN
NGA
GHA
PRY
NIC
BOL
KHM
TZAOMN
BHR
FJI
ISL
KGZ
TTO
GEO
UZB
MLTALB
MDG
LBN
NPL
MUSAFG
CYP
JOR
SLV
ECUKAZ VEN SAU
HND
DOM
SWZ MDA BIH
BGD
URY
GTM
ZWE
CRI
MKDPAN
GRC IRN
LVA KEN
HRV
LKA
SRB
CHL
MAR PER
SYR
TUN
ARE
BLR
EST LTU
NOR
EGY
COL
NZLVNM
ARG AUSMEX
PRT
ISR
PHL
ROM
PAK
UKR
FIN
SVKIRL
SVNBGR
CAN
HUN
DNK
POL
RUS
MYS
TUR ESP
AUT
CHE
SWE
CZE
BRA
BEL
ZAF
GBR
IDN
THA
KOR FRA
NLD
IND JPN
ITA
GERCHN
GNB
CPV
VUT
VUT
GNB
ERI TCD AFG
COM
WSM
SLE
DJI CAF
ATG
BMU
ARM
GMB MRT RWA
KGZ
COG
NER
CPV BDI
BEN
MWI
MNG
BFAGEO
TGO
MOZ
MLI
KHMNPL
FJI GIN
GUY
PNGBIH AZE
ZWE
BLZ
YEM
MDA
BRB
ZMB
ALB
SWZ
UGA
CMR
GAB
SEN
SDNCUB
QAT
MDG
SYR
KAZ
BHR
ETH
BHS
CHN
TZA
GHA
BLR
NIC
EST
GERJPN
KEN
PAK
LVA
MKD
DOM
HND
KWTBGD
MLT
LTU
OMN
ITAIND
VNM
JAM
MUS
PRY
BGR
TTO
UKR
PAN
BOL
LKA
URY
SLV
JOR
USA
IRN
NGA
LBN
ISL CYP
THA
SVN
IDN
NLD
FRA
KOR
SVK HUN
BRA
GTM
ZAF
CZE
TUR
PMYS
PHL
HL
PER
TUN
SWE
GBR
POL
ECU
RUS
ISR
ROM
BWA CRI
BEL
MAR
CHE
ARE
EGY
AUT
ESP
ARG
DNK
SRB
FIN
COL
HRV
DZA
NZL
IRL
CHL
MEX
PRT
VEN
NOR
GRC
SAU AUSCAN
0
Export Concentration
PLW COM
−10
−8
−6
−4
−2
0
−10
Log of GDP relative to the US
(d) Extensive margin of imports
Mean of the log index from 1992−2009
6
Intensive Theil index − HS 6 digit
Mean of the log index from 1992−2009
PAN
BMU
STP
KIR
BHS
IND
PHL
KOR
UKR
MLT CYP
JPN
USA
ZAF
MYS
THA
BLRIRL
LBN
LTU SRB
ISR
ITAGER
GRC NLD ESP
PRY
JAM
ATG
SVK
BHR
HUN
SEN
FIN
MDA
DZA
OMN
MAR
JOR
AFG
CHL
ARM
BRA
CHE
KEN
KHM
BGD
BWA
EGY
PAK
BGR
SWE
ETH
BEL
TZA
MDG
TTO
SAUTUR
URY
CHN
NZL
PRT
GEO
YEM
TGO
UGA
LKA
HND
BFA
ROM
IDNMEXFRA
GTM
MLI
DOM
CZE
ARE
GIN
NIC
SLV
QAT
BEN
PER
HRV
GHA
AUS
CRI
ECU
CUB
VNM
ISL
ZMB
NER
MUS
AUTPOL
SDN
KWT
TUN
NOR
MNG
MKD
NGA
BOL
AZE
VUT ZWE
IRN
GBR
COL
VEN
RUS
SWZ
MRT
SVN
FJI
MWI
KGZ
NPL
CMR
CAN
SYR KAZDNK
EST
LVA
ALB
ARG
GUY
DJIBLZ
GAB
MOZ
BDI BRB
GMB
PNG
BIH
RWA
CPV
CAF
COG
COMWSM
SLE
ERI TCD
GNB
0
MLI
RUS
COG GAB
BWA
COL
ZMB
BFA
JAM SDN DZA
TCD
SLE MWI
KAZ
CRI
TTO
GIN
ZWE NER
CHL
PER
CMR
EGY AUS
PAN
GHA
PRY
KEN CUB
PHL
MRT MLT
MOZBHR
IRL
MYS
BEN
VNM
ZAF
ETH
UGA
MEX
PNG LVA
MUS
ARG
ISL
ISR
BDI SWZ
GUY
CAF
CAN
BOL
MNG
BHS
BRA
KGZ
RWA
GNB
KOR
WSM BMUTGO
JOR
HND
NPL
ARM
TZA
IND
LTUGTM
IDN
GEO
BGD
GMBBLZFJI
BLR
ATG
MDG
NZLHUN
PAK
SLV
FIN UKR
THA
JPN
SVK
KHM
NIC
SEN
TUN
URY
MDA
LBN
SWE NLD ESPFRA
DOM
MAR
CYP
STP
GBR CHN
PRT
VUT
BRB
EST
CHE
DNK
BIH
MKD
LKA
BGR
USA
GRC
CZE
BEL TUR
ALB
GER
SVN
SRB ROMAUTPOL
KIR COM DJI
HRV
AFG
ERI
ITA
CPV
4
Import Concentration
4
IRN
ARE
NGA
OMN
SAU
YEM
SYR
KWT VEN
NOR
QAT
AZE ECU
2
6
Intensive Theil index − HS 6 digit
0
2
0
R2=0.56
(c) Extensive margin of exports
Export Concentration
−5
Log of GDP relative to the US
R2=0.75
−10
−5
0
−10
Log of GDP relative to the US
−5
0
Log of GDP relative to the US
R2=0.01
R2=0.14
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 12: The relationship of export and import concentration verus GDP across 160 countries based on gross
trade flows.
50